The influence of anthropogenic CO₂ emissions and emissions scenarios on the carbon–climate system is the primary driver of ocean and terrestrial sinks as the major negative feedbacks that determine the atmospheric CO₂ levels, which then drive climate feedbacks through radiative forcing (Figure 5.2) (Friedlingstein et al., 2006; Jones et al., 2013; Jones and Friedlingstein, 2020). Biogeochemical feedbacks follow as an outcome of both carbon and climate forcing on the physics and the biogeochemical processes of the ocean and terrestrial carbon cycles (Figure 5.2) (Katavouta et al., 2018; Williams et al., 2019; Jones and Friedlingstein, 2020). Together, these carbon–climate feedbacks can amplify or suppress climate change by altering the rate at which CO₂ builds up in the atmosphere through changes in the land and ocean sources and sinks (Figure 5.2; C.D. Jones et al., 2013; Raupach et al., 2014; Williams et al., 2019). These changes depend on the, often non-linear, interaction of the drivers (CO₂ and climate) and processes in the ocean and land as well as the emissions scenarios (Figure 5.2; Sections 5.4 and 5.6) (Raupach et al., 2014; Schwinger et al., 2014; Williams et al., 2019). There is high confidence that carbon–climate feedbacks and their century scale evolution play a critical role in two linked climate metrics that have significant climate and policy implications: (i) the fraction of anthropogenic CO₂ emissions that remains in the atmosphere, the so-called airborne fraction of CO₂ (AF; Section 5.2.1.2, Figures 5.2 and 5.7, and FAQ 5.1); and (ii) the quasi-linear trend characteristic of the transient temperature response to cumulative CO₂ emissions (TCRE; Section 5.5; MacDougall, 2016; Williams et al., 2016; Jones and Friedlingstein, 2020) and other GHGs (CH₄ and N₂O). This chapter assesses the implications of these issues from the perspective of carbon cycle processes (Figure 5.2) in Section 5.2 (historical and contemporary), Section 5.3 (changing carbonate chemistry), Section 5.4 (future projections), Section 5.5 (remaining carbon budget) and Section 5.6 (response to carbon dioxide removal and solar radiation modification).

The airborne fraction is an important constraint for adjustments in carbon–climate feedbacks and reflects the partitioning of CO₂ emissions between reservoirs by the negative feedbacks, which were 31% on land and 23% in the ocean for the decade 2010–2019 and also dominated the historical period (Figure 5.2; Table 5.1) (Friedlingstein et al., 2020). During the period 1959–2019, the airborne fraction has largely followed the growth in anthropogenic CO₂ emissions with a mean of 44% and a large interannual variability (Ballantyne et al., 2012; Ciais et al., 2019; Friedlingstein et al., 2020, Section 5.2.1.2; Table 5.1). The negative feedback to CO₂ concentrations is associated with its impact on the air–sea and air–land CO₂ exchange through strengthening of partial pressure of CO₂ (pCO₂) gradients as well as the internal processes that enhance uptake. Two of these key processes are the buffering capacity of the ocean and the CO₂ fertilization effect on gross primary production (Sections 5.4.1–5.4.4).

Paleoclimatic proxy records extend beyond the variability of recent decadal climate oscillations and thus provide an independent perspective on feedbacks between climate and carbon cycle dynamics. According to reconstructions, these past changes were slower than the current anthropogenic ones, so they cannot provide an unequivocal comparison. Nonetheless, they can help appraise sensitivities and point towards potentially dominant mechanisms of change (Tierney et al., 2020) on (sub)centennial to (multi)millennial time scales.

In the past, atmospheric CO₂ concentrations reached much higher levels than present day (Cross-Chapter Box 2.1 and Figure 5.3). In particular, the Paleocene–Eocene thermal maximum (PETM), 55.9–55.7 Ma (Figure 5.3), provides some level of comparison with the current and projected anthropogenic increase in CO₂ emissions (Chapter 2). Atmospheric CO₂ concentrations increased from about 900 to around 2000 ppm in 3–20 kyr as a result of geological carbon release to the ocean–atmosphere system (Zeebe et al., 2016; Gutjahr et al., 2017; Cui and Schubert, 2018; Kirtland Turner, 2018). There is low to medium confidence in evaluations of the total amount of carbon released during the PETM, as proxy data constrained estimates vary from around 3000 to more than 7000 PgC, with methane hydrates, volcanic emissions, terrestrial and/or marine organic carbon, or some combination thereof, as the probable sources of carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., 2017; Elling et al., 2019; Jones et al., 2019; Haynes and Hönisch, 2020). Methane emissions related to hydrate/permafrost thawing and fossil carbon oxidation may have acted as positive feedbacks (Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al., 2019), as the inferred increase in atmospheric CO₂ can only account for approximately half of the reported warming (Zeebe et al., 2009). The estimated, time-integrated carbon input is broadly similar to the RCP8.5 extension scenario, although CO₂ emissions rates (0.3–1.5 Pg yr^–1) and by inference the rate of CO₂ accumulation in the atmosphere (4–42 ppm per century) during the PETM were at least 4–5 lower than during the modern era (from 1995 to 2014; Table 2.1; Zeebe et al., 2016; Gingerich, 2019).

The Antarctic ice core record covering the past 800 kyr provides an important archive to explore the carbon–climate feedbacks prior to anthropogenic perturbations (Brovkin et al., 2016). Polar ice cores represent the only climatic archive from which past GHG concentrations can be directly measured. Major GHGs, CH₄, N₂O and CO₂ generally co-vary on orbital time scales (Loulergue et al., 2008; Lüthi et al., 2008; Schilt et al., 2010b; Chapter 2), with consistently higher atmospheric concentrations during warm intervals of the past, pointing to a strong sensitivity to climate (Figure 5.4). Modelling work suggests that the carbon cycle contributed to globalise and amplify changes in orbital forcing, which are pacing glacial–interglacial climate oscillations (Ganopolski and Brovkin, 2017), with ocean biogeochemistry and physics, terrestrial vegetation, peatland, permafrost and exchanges with the lithosphere including chemical weathering, volcanic activity, sediment burial and marine calcium carbonate compensation all playing a role in modulating the concentration of atmospheric GHGs.

Atmospheric GHG concentrations were much less variable during the pre-industrial Holocene (from 11.7 ka to 1750 CE). Atmospheric CH₄ concentrations decreased at the beginning of the Holocene, consistent with a general weakening of boreal sources (Yang et al., 2017; Beck et al., 2018) and further decline during the mid-Holocene owing to a reduction in Southern Hemisphere emissions concomitant with a southward shift of the ITCZ (Singarayer et al., 2011; Beck et al., 2018). Atmospheric CH₄ concentrations increased about 5 ka, which prompted the hypothesis of an early anthropogenic influence related to land-use changes in South East Asia (Ruddiman et al., 2016). However, stable isotope compositions on CH₄ extracted from Greenland and Antarctic ice (Beck et al., 2018) reveal that natural emissions located in the southern tropics were responsible for the rise in atmospheric CH₄ concentrations, in line with model simulations (Singarayer et al., 2011) thus disputing the early anthropogenic influence on the global CH₄ budget. Atmospheric N₂O concentrations increased slightly (20 ppb) across the Holocene, associated with a gradual decline in its nitrogen stable isotope composition (H. Fischer et al., 2019). The combined signal is consistent with a small increase in terrestrial emissions, offset by a reduction in marine emissions (Schilt et al., 2010b; Fischer et al., 2019).

The early Holocene decrease in CO₂ concentration by about 5 ppm (Schmitt et al., 2012) has been attributed to post-glacial regrowth in terrestrial biomass and a gradual increase in peat reservoirs over the Holocene, resulting in the sequestration of several hundred PgC (Yu et al., 2010; Nichols and Peteet, 2019). Peat accumulation rates in boreal and temperate regions were higher under warmer summer conditions in the early to mid-Holocene (Loisel et al., 2014; Stocker et al., 2017). The 20 ppm gradual increase of atmospheric CO₂ starting 7 ka has been attributed to a decrease in natural terrestrial biomass due to climate change, carbonate compensation and enhanced shallow water carbonate deposition (Menviel and Joos, 2012; Brovkin et al., 2016), consistent with stable carbon isotope measurements on CO₂ extracted from Antarctic ice (Elsig et al., 2009; Schmitt et al., 2012). These isotopic measurements do not support an early anthropogenic influence on atmospheric CO₂ due to land-use change and forest clearing (Ruddiman et al., 2016). Recent paleoceanographic evidence suggests that remineralized carbon outgassing associated with increased Southern Ocean circulation and upwelling (Studer et al., 2018), possibly promoted by stronger Southern Hemisphere westerly winds (Saunders et al., 2018), could have additionally contributed to the late Holocene increase in atmospheric CO₂ concentrations. However, the role of these mechanisms remained insignificant in transient Holocene ESM simulations (Brovkin et al., 2019). Overall, as in AR5 (WGI, Chapter 5), there is medium confidence in the key drivers of the CO₂ increase between the early Holocene and the beginning of the industrial era, yet there is low confidence in the relative contributions of these drivers due to insufficient quantitative constraints on particular processes.

This section assesses the trends and variability in atmospheric accumulation of the three main greenhouse gases (GHGs) – CO₂, CH₄ and N₂O – their ocean and terrestrial sources and sinks as well as their budgets during the Industrial Era (1750–2019). Emphasis is placed on the more recent contemporary period (1959–2019) where understanding is increasingly better constrained by atmospheric, ocean and land observations. The section also assesses our increased understanding of the anthropogenic forcing and processes driving the trends, as well as how variability at the seasonal to decadal scales provide insights on the mechanism governing long-term trends and emerging biogeochemical–climate feedbacks with their regional characteristics.

There are two anthropogenic sources of carbon dioxide (CO₂): fossil emissions and net emissions (including removals) resulting from land-use change and land management (also shown in this chapter as LULUCF: land use, land-use change, and forestry; in previous IPCC reports it has been termed forestry and other land use, FOLU). Fossil CO₂ emissions include the combustion of the fossil fuels coal, oil and gas, covering all sectors of the economy (electricity, transport, industrial, and buildings), fossil carbonates such as in cement manufacturing, and other industrial processes such as the production of chemicals and fertilizers (Figure 5.5a). Fossil CO₂ emissions are estimated by combining economic activity data and emissions factors, with different levels of methodological complexity (tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse Gas Inventories). Several organizations or groups provide estimates of fossil CO₂ emissions, with each dataset having slightly different system boundaries, methods, activity data, and emissions factors (Andrew, 2020). Datasets cover different time periods, which can dictate the datasets and methods that are used for a particular application. The data reported here is from an annually updated data source that combines multiple sources to maximise temporal coverage (Friedlingstein et al., 2020). The uncertainty in global fossil CO₂ emissions is estimated to be ±5% (1 standard deviation).

Fossil CO₂ emissions have grown continuously since the beginning of the industrial era (Figure 5.5) with short intermissions due to global economic crises or social instability (Peters et al., 2012; Friedlingstein et al., 2020). In the most recent decade (2010–2019), fossil CO₂ emissions reached an average 9.6 ± 0.5 PgC yr^–1 and were responsible for 86% of all anthropogenic CO₂ emissions. In 2019, fossil CO₂ emissions were estimated to be 9.9 ±0.5 PgC yr^–1excluding carbonation (Friedlingstein et al., 2020), the highest on record. These estimates exclude the cement carbonation sink of around 0.2 PgC yr^–1. Fossil CO₂ emissions grew at 0.9% yr^–1 in the 1990s, increasing to 3.0% yr^–1 in the 2000s, and reduced to 1.2% from 2010 to 2019. The slower growth in fossil CO₂ emissions in the last decade is due to a slowdown in growth from coal use. CO₂ emissions from coal use grew at 4.8% yr^–1 in the 2000s, but slowed to 0.4% yr^–1 in the 2010s. CO₂ emissions from oil use grew steadily at 1.1% yr^–1 in both the 2000s and 2010s. CO₂ emissions from gas use grew at 2.5% yr^–1 in the 2000s and 2.4% yr^–1 in 2010s, but has shown signs of accelerated growth of 3% yr^–1 since 2015 (Peters et al., 2020). Direct CO₂ emissions from carbonates in cement production are around 4% of total fossil CO₂ emissions, and grew at 5.8% yr^–1 in the 2000s but a slower 2.4% yr^–1 in the 2010s. The uptake of CO₂ in cement infrastructure (carbonation) offsets about one half of the carbonate emissions from current cement production (Friedlingstein et al., 2020). These results are robust across the different fossil CO₂ emissions datasets, despite minor differences in levels and rates, as expected given the reported uncertainties (Andrew, 2020). During 2020, the COVID-19 pandemic led to a rapid, temporary decline in fossil CO₂ emissions, estimated to be around 7% based on a synthesis of four estimates. (Cross-Chapter Box 6.1; Forster et al., 2020; Friedlingstein et al., 2020; Le Quéré et al., 2020; Liu et al., 2020).

For the decade 2010–2019, average emissions were estimated at 1.6 ± 0.7 PgC yr^–1 (mean ± standard deviation, 1 sigma; Friedlingstein et al., 2020). Alikely general upward trend since 1850 is reversed during the second part of the 20th century (Figure 5.5b). Trends since the 1980s have low confidence because they differ between estimates, which is related, among other things, to Houghton and Nassikas (2017) using a different land-use forcing than Hansis et al. (2015) and the DGVMs. Higher emissions estimates are expected from DGVMs run under transient environmental conditions compared to bookkeeping estimates, because the DGVM estimate includes the loss of additional sink capacity. Because the transient setup requires a reference simulation without land-use change to separate anthropogenic fluxes from natural land fluxes, LULUCF estimates by DGVMs include the sink forests that would have developed in response to environmental changes on areas that in reality have been cleared (Pongratz et al., 2014). The agricultural areas that replaced these forests have a reduced residence time of carbon, lacking woody material, and thus provide a substantially smaller additional sink over time (Gitz and Ciais, 2003). The loss of additional sink capacity is growing in particular with atmospheric CO₂ and increases DGVM-based LULUCF flux estimates relative to bookkeeping estimates over time (Figure 5.5).

More evidence on the pre-industrial LULUCF flux has emerged since AR5 in the form of new estimates of cumulative carbon losses until today, and of a better understanding of natural carbon cycle processes over the Holocene (Ciais et al., 2013). Cumulative carbon losses by land-use activities since the start of agriculture and forestry (pre-industrial and industrial era) have been estimated at 116 PgC based on global compilations of carbon stocks for soils (Sanderman et al., 2017) with about 70 PgC of this occurring prior to 1750, and for vegetation as 447 PgC (inner quartiles of 42 calculations: 375–525 PgC) (Erb et al., 2018). Emissions prior to 1750 can be estimated by subtracting the post-1750 LULUCF flux from Table 5.1 from the combined soil and vegetation losses until today; they would then amount to 328 (161–501) PgC assuming error ranges are independent. A share of 353 (310–395) PgC from prior to 1800 has indirectly been suggested as the difference between net biosphere flux and terrestrial sink estimates, which is compatible with ice-core records due to a low airborne fraction of anthropogenic emissions in pre-industrial times (Erb et al., 2018; see also Section 5.1.2.3). Low confidence is assigned to pre-industrial emissions estimates.

Since AR5, evidence emerged that the LULUCF flux might have been underestimated as DGVMs include anthropogenic land cover change, but often ignore land management practices not associated with a change in land cover; land management is more widely captured by bookkeeping models through use of observation-based carbon densities (Ciais et al., 2013; Pongratz et al., 2018). Sensitivity studies show that practices such as wood and crop harvesting increase global net LULUCF emissions (Arneth et al., 2017) and explain about half of the cumulative loss in biomass (Erb et al., 2018).

Over the past six decades, the fraction of anthropogenic CO₂ emissions that has accumulated in the atmosphere (referred to as airborne fraction) has remained near constant at approximately 44% (Figure 5.7) (Ballantyne et al., 2012; Ciais et al., 2019; Gruber et al., 2019b; Friedlingstein et al., 2020). This suggests that the land and ocean CO₂ sinks have continued to grow at a rate consistent with the growth rate of anthropogenic CO₂ emissions, albeit with large interannual and sub-decadal variability dominated by the land sinks (Figure 5.7).

Since AR5, an alternative observable diagnostic to the airborne fraction has been proposed to understand the trends in land and ocean sinks in response to its driving atmospheric CO₂ concentrations (Raupach et al., 2014; Bennedsen et al., 2019). It is the sink rate that is defined as the combined ocean and land sink flux per unit of atmospheric excess of CO₂ above pre-industrial levels (Raupach et al., 2014). The sink rate has declined over the past six decades, which indicates that the combined ocean and land sinks are not growing as fast as the growth in atmospheric CO₂ (Raupach et al., 2014; Bennedsen et al., 2019). Possible explanations for the sink rate decline are that the land and/or ocean CO₂ sinks are no longer responding linearly with CO₂ concentrations or that anthropogenic emissions are slower than exponential (Figure 5.7 and Sections 5.2.1.3 and 5.2.1.4; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., 2019). In addition, both diagnostics are influenced by major climate modes (e.g., ENSO) and volcanic eruptions that contribute to high interannual variability (Gloor et al., 2010; Frölicher et al., 2013; Raupach et al., 2014), suggesting high sensitivity to future climate change. Uncertain land-use change fluxes (Section 5.2.1.2) influence the robustness of the trends. Based on the airborne fraction (AF), it is concluded with medium confidence that both ocean and land CO₂ sinks have grown consistent with the rising of anthropogenic emissions. Further research is needed to understand the drivers of changes in the CO₂ sink rate.

Since AR5 and SROCC, major advances in globally coordinated ocean CO₂ observations (Surface Ocean CO₂ Atlas, SOCAT; and Global Ocean Data Analysis Project, GLODAP), the harmonization of ocean and coastal-observation-based products, atmospheric and oceanic inversion models and forced global ocean biogeochemical models (GOBMs) have increased the level of confidence in the assessment of trends and variability of air–sea fluxes and storage of CO₂ in the ocean during the historical period (1960–2018; see also Supplementary Materials 5.SM.1; Ciais et al., 2013; Bakker et al., 2016; Landschützer et al., 2016, 2020; Bindoff et al., 2019; DeVries et al., 2019; Gregor et al., 2019; Gruber et al., 2019a, b; Tohjima et al., 2019; Friedlingstein et al., 2020; Hauck et al., 2020; Olsen et al., 2020). A major advance since SROCC is that, for the first time, all six published observational product fluxes used in this assessment, are made more comparable using a common ocean and sea ice cover area, integration of climatological coastal fluxes scaled to increasing atmospheric CO₂ and an ensemble mean of ocean fluxes calculated from three re-analysis wind products (Supplementary Materials 5.SM.2; Landschützer et al., 2014, 2020; Rödenbeck et al., 2014; Zeng et al., 2014; Denvil-Sommer et al., 2019; Gregor et al., 2019; Iida et al., 2021). From a process point of view, the ocean uptake of anthropogenic carbon is a two-step set of abiotic processes that involves the exchange of CO₂, first across the air–sea boundary into the surface mixed layer, followed by its transport into the ocean interior where it is stored for decades to millennia, depending on the depth of storage (Gruber et al., 2019b). Two definitions of air–sea fluxes of CO₂ are used in this assessment for both observational products and models: S_ocean is the global mean ocean CO₂ sink and F_net denotes the net spatially varying CO₂ fluxes (Hauck et al., 2020). Adjustment of the mean global F_net for the pre-industrial sea-to-air CO₂ flux associated with land-to-ocean carbon flux term makes F_net comparable to S_ocean (Jacobson et al., 2007; Resplandy et al., 2018; Hauck et al., 2020).

There are multiple lines of observational and modelling evidence that support with high confidence the finding that, in the historical period (1960–2018), air–sea fluxes and storage of anthropogenic CO₂ are largely influenced by atmospheric CO₂ concentrations, physical ocean processes and physicochemical carbonate chemistry, which determines the unique properties of CO₂ in seawater (Chapter 9 and Cross-Chapter Box 5.3; Wanninkhof, 2014; DeVries et al., 2017; McKinley et al., 2017, 2020, Gruber et al., 2019a, b; Hauck et al., 2020). Here we assess three different approaches (Figures 5.8a,b and 5.9) that together provide high confidence that, during the historical period (1960–2018), the ocean carbon sink (S_ocean) and its associated ocean carbon storage have grown in response to global anthropogenic CO₂ emissions (Gruber et al., 2019a; Hauck et al., 2020; McKinley et al., 2020).

In the first assessment approach, the mean global multi-decadal (1960–2019) trends in the ocean sink (S_ocean) for CO₂ show a high degree of coherence across the nine GOBMs and sixpCO₂ -based observational product reconstructions (1987–2018) which, despite a temporary slowdown (or ‘hiatus’) in the 1990s, is also quasi-linear over that period (Figure 5.8a; Gregor et al., 2019; Hauck et al., 2020). This coherence between the GOBMs and observations-based reconstructions (1987–2018; r²=0.85) provides high confidence that the ocean sink (S_ocean in Section 5.2.1.5) evaluated from GOBMs (1960–2019) grew quasi-linearly from 1.0 ± 0.3 PgC yr^–1 to 2.5 ± 0.6 PgC yr^–1 between the decades 1960–1969 and 2010–2019 in response to global CO₂ emissions (Figure 5.8a; Table 5.1; Friedlingstein et al., 2020; Hauck et al., 2020). The cumulative ocean CO₂ uptake (105 ± 20 PgC) is 23% of total anthropogenic CO₂ emissions (450 ± 50 PgC) for the same period (Friedlingstein et al., 2020). Notwithstanding the high confidence in the magnitude of the annual to decadal trends for S_ocean, this assessment is moderated to mediumconfidence by the low confidence in the currently inadequately constrained uncertainties in the pre-industrial land-to-ocean carbon flux, the uncertain magnitude of winter outgassing from the Southern Ocean, and the uncertain effect of the ocean surface cool-skin, the effect of data sparsity, differences between wind products and the uncertain contribution from the changing land–ocean continuum on global and regional fluxes (Jacobson et al., 2007; Resplandy et al., 2018; Roobaert et al., 2018; Bushinsky et al., 2019; Hauck et al., 2020; Watson et al., 2020; Gloege et al., 2021). However, both GOBMs and pCO₂ -based observational products independently reveal a slowdown or ‘hiatus’ of the ocean sink in the 1990s, which provides a valuable constraint for model verification and leads to greater confidence in the model outputs (Figure 5.8a; Landschützer et al., 2016; Gregor et al., 2018; DeVries et al., 2019; Hauck et al., 2020). A number of studies point to the role of the Southern Ocean in the global ‘1990s hiatus’ in air–sea CO₂ fluxes, but provide different process-based explanations linking ocean temperature, mixing and meridional overturning circulation (MOC) responses to variability in large-scale climate systems, wind stress and volcanic activity, as well as the sensitivity of the air–sea CO₂ flux to small changes in the atmospheric forcing from anthropogenic CO₂ (Landschützer et al., 2016; DeVries et al., 2017; Bronselaer et al., 2018; Gregor et al., 2018; Gruber et al., 2019a; Keppler and Landschützer, 2019; McKinley et al., 2020; Nevison et al., 2020). Data sparsity in the Southern Ocean could also be a factor amplifying the global decadal perturbation of the 1990s (Gloege et al., 2021). Therefore, while there is high confidence in the 1990s hiatus of the global ocean sink for anthropogenic CO_2, and that the Southern Ocean makes an observable contribution to it, there is still low confidence in the attribution for the processes behind the 1990s hiatus (Section 5.2.1.3.2). Observed increases in the amplitude of the seasonal cycle of oceanpCO₂ and reductions in the mean global buffering capacity provide high confidence that the growing CO₂ sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry (Jiang et al., 2019). However, there is medium confidence that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of S_ocean (Sections 5.1.2; 5.3.2 and 5.4.2, and Figure 5.8a; SROCC (Section 5.2.2.3.2; Bates et al., 2014; Sutton et al., 2016; Fassbender et al., 2017; Landschützer et al., 2018; Jiang et al., 2019). A recent model-based study suggests that re-emergence of previously stored anthropogenic CO₂ is changing the buffering capacity of the mixed layer and reducing the ocean sink for anthropogenic CO₂ during the historical period (Rodgers et al., 2020). This trend is not reflected in observations-based products (Figure 5.8a), so we attribute a low confidence.

Here we provide a third comparative assessment approach depicting the spatial coherence of ocean air–sea fluxes and storage rates of CO₂ as well as a quantitative assessment of both fluxes for the same period (1994–2007; Figure 5.9). Observation-based pCO₂ flux products show that emissions of natural CO₂ occur mostly in the tropics and high-latitude Southern Ocean, and that the uptake and storage of anthropogenic CO₂ occurs predominantly in the mid-latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong ocean CO₂ sink regions are those in the mid-latitudes associated with the cooling of poleward flowing subtropical surface waters as well as equatorward flowing sub-polar surface waters, both of which contribute to the formation of Mode, Intermediate and Deep water masses that transport anthropogenic CO₂ into the ocean interior on time scales of decades to centuries in both hemispheres (Section 9.2.2.3 and Figure 5.9; DeVries, 2014; Gruber et al., 2019b; Wu et al., 2019). The mean decadal scale magnitude and uncertainties of S_ocean from net air sea fluxes (F_net ) were calculated from an ensemble of six observational-based product reconstructions (Figure 5.9a) and the storage rates in the ocean interior derived from multiple ocean interior CO₂ datasets (Gruber et al., 2019b; Figure 5.9b). The cumulative CO₂ stored in the ocean interior from 1800 to 2007 has been estimated at 140 ±18 PgC (Gruber et al., 2019b). As reported in SROCC (Section 5.2.2.3.1; IPCC, 2019b), the net ocean CO₂ storage between 1994–2007 was 29 ± 4 PgC, which corresponds to a mean storage of 26 ± 5% of anthropogenic CO₂ emissions for that period (Gruber et al., 2019b). The resulting net annual storage rate of anthropogenic CO₂, equivalent to S_ocean for the period mid-1994 to mid-2007 is 2.2 ± 0.3 PgC yr^–1, which is in very close agreement with the top-down air–sea flux estimate of S_ocean of 2.1 ± 0.5 PgC yr^–1 from GOBMs and 1.9 ± 0.3PgC yr^–1 frompCO₂ -based observational products with the steady river carbon flux correction of 0.62 PgC yr^–1 for the same time period (Gruber et al., 2019b; Hauck et al., 2020). This close agreement between these independent ocean CO₂ sink estimates derived from air–sea fluxes and storage rates in the ocean interior support the medium confidence assessment that the ocean anthropogenic carbon storage rates continue to be determined by the ocean sink (S_ocean) in response to growing CO₂ emissions (Figure 5.9; McKinley et al., 2020).

Regional decadal-scale anomalies in the variability of ocean CO₂ storage have also emerged, probably associated with changes in the MOC, which may influence the global variability in F_net (Chapter 9; DeVries et al., 2017). In the interior of the Indian and Pacific sectors of the Southern Ocean, and the North Atlantic, the increase in the CO₂ inventory from 1994 to 2007 was about 20% smaller than expected from the atmospheric CO₂ increase during the same period and the anthropogenic CO₂ inventory in 1994 (Sabine eta al., 2004; Gruber et al., 2019a). There is medium confidence that the ocean CO₂ inventory strengthened again in the decade 2005–2015 (DeVries et al., 2017). In the North Atlantic, a low rate of anthropogenic CO₂ storage at 1.9 ± 0.4 PgC per decade during the time period of 1989–2003 increased to 4.4 ± 0.9 PgC per decade during 2003–2014. This is associated with changing ventilation patterns driven by the North Atlantic Oscillation (Woosley et al., 2016). In the Pacific sector of the Southern Ocean, the rate of anthropogenic CO₂ storage also increased from 8.8 ± 1.1 (1σ) PgC per decade during 1995–2005 to 11.7 ± 1.1 PgC per decade during 2005–2015 (Carter et al., 2019). However, in the Subantarctic Mode Water of the Atlantic sector of the Southern Ocean, the storage rate of the anthropogenic CO₂ was rather lower after 2005 than before (Section 9.2.3.2; Tanhua et al., 2017; Bindoff et al., 2019). These changes have been predominantly ascribed to the impact of changes in the MOC on the transport of anthropogenic CO₂ into the ocean interior due to regional climate variability, in addition to the increase in the atmospheric CO₂ concentration (Section 9.2.3.1; Wanninkhof et al., 2010; Pérez et al., 2013; DeVries et al., 2017, 2019; Gruber et al., 2019b; McKinley et al., 2020). However,the low frequency of carbon observations in the interior of the vast ocean leads to medium confidence in the assessment of temporal variability in the rate of regional ocean CO₂ storage and its controlling mechanisms.

In summary, multiple lines of observational and modelling evidence provide high confidence in the finding that the ocean sink for anthropogenic CO₂ has increased quasi-linearly over the past 60 years in response to growing global emissions of anthropogenic CO_2, with a mean fraction of 23% of total emissions. The high confidence assessment is moderated to medium confidence due to a number of ocean CO₂ flux terms yet to be adequately constrained. Observed changes in the variability of oceanpCO₂ and observed reductions in the mean global buffering capacity provide high confidence that the growing CO₂ sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry. However, there is medium confidence that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of S_ocean.

The global CO₂ budget (Figure 5.12) encompasses all natural and anthropogenic CO₂ sources and sinks. Table 5.1 shows the perturbation of the global carbon mass balance between reservoirs since the beginning of the industrial era, circa 1750.

Over the past decade (2010–2019), 10.9 ± 0.9 PgC yr^–1 were emitted from human activities, which were distributed between three Earth system components: 46% accumulated in the atmosphere (5.1 ± 0.02 PgC yr^–1), 23% was taken up by the ocean (2.5 ± 0.6 PgC yr^–1) and 31% was stored by vegetation in terrestrial ecosystems (3.4 ± 0.9 PgC yr^–1) (Table 5.1). There is a budget imbalance of 0.1 PgCyr^–1 which is within the uncertainties of the other terms. Over the industrial era (1750–2019), the total cumulative CO₂ fossil fuel and industry emissions were 445 ± 20 PgC, and the LULUCF flux (= net land-use change in Figure 5.12) was 240 ± 70 PgC (medium confidence). The equivalent total emissions (685 ± 75 PgC) was distributed between the atmosphere (285 ± 5 PgC), oceans (170 ± 20 PgC) and land (230 ± 60 PgC; Table 5.1), with a budget imbalance of 20 PgC. This budget (Table 5.1) does not explicitly account for source/sink dynamics due to carbon cycling in the land–ocean aquatic continuum comprising freshwaters, estuaries, and coastal areas. Natural and anthropogenic transfers of carbon from soils to freshwater systems are significant (2.4–5.1 PgC yr^–1) (Regnier et al., 2013; Drake et al., 2018). Some of the carbon is buried in freshwater bodies (0.15 PgC) (Mendonça et al., 2017), and a significant proportion returns to the atmosphere via outgassing from lakes, rivers and estuaries (Raymond et al., 2013; Regnier et al., 2013; Lauerwald et al., 2015). The net export of carbon from the terrestrial domain to the open oceans is estimated to be 0.80 PgC yr^–1 (medium confidence), based on the average of (Jacobson et al., 2007; Resplandy et al., 2018) and corrected to account for 0.2 PgC buried in ocean floor sediments. These terms are included in Figure 5.12. Inclusion of other smaller fluxes could further constrain the carbon budget (Ito, 2019; Friedlingstein et al., 2020).

Methane is a much more powerful greenhouse gas than CO₂ (Chapter 7) and participates in tropospheric chemistry (Chapter 6). The CH₄ variability in the atmosphere is mainly the result of the net balance between the sources and sinks on the Earth’s surface and chemical losses in the atmosphere. Atmospheric transport evens out the regional CH₄ differences between different parts of the Earth’s atmosphere. The steady-state lifetime is estimated to be 9.1 ± 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the loss of atmospheric CH₄ occurs in the troposphere by reaction with hydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest 5% by chemical reactions with OH, excited state oxygen (O¹D), and atomic chlorine (Cl) in the stratosphere (Saunois et al., 2020). Methane has large emissions from natural and anthropogenic origins, but a clear demarcation of their nature is difficult because of the use and conversions of the natural ecosystem for human activities. The largest natural sources are from wetlands, freshwater and geological process, while the largest anthropogenic emissions are from enteric fermentation and manure treatment, landfills and waste treatment, rice cultivation and fossil fuel exploitation (Table 5.2). In the past two centuries, CH₄ emissions have nearly doubled, predominantly human driven since 1900, and persistently exceeded the losses (virtually certain), thereby increasing the atmospheric abundance as evidenced from the ice core and firn air measurements (Ferretti et al., 2005; Ghosh et al., 2015).

The positive gradient between CH₄ at Cape Grim, Australia (41°S) and Trinidad Head, USA (41°N), and the bigger difference between Trinidad Head and global mean CH₄ compared to that between global mean CH₄ and Cape Grim, strongly suggest that the Northern Hemisphere is the dominant origin of anthropogenic CH₄ emissions (Figure 5.13). The loss rate of CH₄ in troposphere does not produce a large positive north–south hemispheric gradient in CH₄ due to parity in hemispheric mean OH concentration (Patra et al., 2014), or in the case of greater OH concentrations in the northern rather than the Southern Hemisphere as simulated by the chemistry-climate models (Naik et al., 2013). Coal mining contributed about 35% of the total CH₄ emissions from all fossil fuel-related sources. Top-down estimates of fossil fuel emissions (106 Tg yr^–1) are smaller than bottom-up estimates (115 Tg yr^–1) during 2008–2017 (Table 5.2). Inventory-based estimates suggest that CH₄ emissions from coal mining increased by 17 Tg yr^–1 between the periods 2002–2006 and 2008–2012, with a dominant contribution from China (Peng et al., 2016; Crippa et al., 2020; Höglund-Isaksson et al., 2020). Inventory-based estimates suggest that CH₄ emissions from coal mining increased by 17 Tg yr^–1 between the periods 2002–2006 and 2008–2012, with a dominant contribution from China (Peng et al., 2016; Crippa et al., 2020; Höglund-Isaksson et al., 2020). Recent country statistics and detailed inventory-based estimates show that CH₄ emissions from coal mining in China declined between 2012 and 2016 (Sheng et al., 2019; Gao et al., 2020), while atmospheric-based estimates suggest a continuation of CH₄ emissions growth but at a slower rate to the year 2015 (Miller et al., 2019) and 2016 (Chandra et al., 2021). Emissions from oil and gas extraction and use decreased in the 1980s and 1990s, but increased in the 2000s and 2010s (Dlugokencky et al., 1994; Stern and Kaufmann, 1996; Howarth, 2019; Crippa et al., 2020). The attribution to multiple CH₄ sources using spatially aggregated atmospheric d¹³C data remained underdetermined to infer the global total emissions from the fossil fuel industry, biomass burning and agriculture (Rice et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; Thompson et al., 2018).

Biomass burning and biofuel consumption (including natural and anthropogenic processes) caused at least 30 Tg yr^–1emissions during 2008–2017 and constituted up to about 5% of global anthropogenic CH₄ emissions. Methane emissions from open biomass burning decreased during the past two decades mainly due to reduction of burning in savanna, grassland and shrubland (van der Werf et al., 2017; Worden et al., 2017). There is recent evidence from the tropics that fire occurrence is non-linearly related to precipitation, implying that severe droughts will increase CH₄ emissions from fires, particularly from the degraded peatlands (Field et al., 2016).

A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in the growth of atmospheric methane abundance, in particular after 2006 (Rigby et al., 2017; Turner et al., 2017). Studies applying three-dimensional atmospheric inversion (McNorton et al., 2018), simple multi-species inversion (Thompson et al., 2018), as well as empirical methods using a variety of observational constraints based on OH chemistry (Nicely et al., 2018; Patra et al., 2021), do not find trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry–climate models based on fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate forcers simulate an increase in OH over this period (Zhao et al., 2019; Stevenson et al., 2020; see Section 6.2.3). These contrasting lines of evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 (low confidence).

Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant trends (high agreement between models, medium evidence). Thus, the inter-decadal difference of total CH₄ emissions derived from inversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest that progress towards atmospheric CH₄ quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) emissions in Europe, Russia and temperate North America over 1988–2000. In the global totals, emissions equalled loss in the early 2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997–2017), West Asia (2005–2017), Brazil (1988–2017) and Northern Africa (2005–2017), and fossil fuel exploitations in temperate North America (2010–2017; Lan et al., 2019; Crippa et al., 2020; Höglund-Isaksson et al., 2020; Jackson et al., 2020; Chandra et al., 2021).

There is evidence from emissions inventories at country level and regional scale inverse modelling that CH₄ growth rate variability between 1988 and 2017 is closely linked to anthropogenic activities (medium agreement). Isotopic composition observations and inventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH₄ growth since 2007 (high confidence). Shorter-term decadal variability is predominantly driven by the influence of El Niño–Southern Oscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations (medium confidence), but lacking quantitative contribution from each of the sectors. By synthesizing all available information regionally from a priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), the capacity to track and explain changes in, and drivers of, natural and anthropogenic CH₄ regional and global emissions has improved since AR5, but fundamental uncertainties related to OH variations remain unchanged.

The human perturbation of the natural nitrogen cycle through the use of synthetic fertilizers and manure, as well as nitrogen deposition resulting from land-based agriculture and fossil fuel burning has been the largest driver of the increase in atmospheric N₂O of 31.0 ± 0.5 ppb (10%) between 1980 and 2019 (robust evidence, high agreement) (Tian et al., 2020). The long atmospheric lifetime of N₂O implies that it will take more than a century before atmospheric abundances stabilize after the stabilization of global emissions. The rise of atmospheric N₂O is of concern, not only because of its contribution to the anthropogenic radiative forcing (Chapter 7) but also because of the importance of N₂O in stratospheric ozone loss (Ravishankara et al., 2009; Fleming et al., 2011; W. Wang et al., 2014).

The AR5 (WGI, Section 6.4.3) and SRCCL (Section 2.3.3) concluded that agriculture is the largest anthropogenic source of N₂O emissions. Since SRCCL (2.3.3), a new synthesis of inventory-based and modelling studies shows that the widespread use of synthetic fertilizers and manure on cropland and pasture, manure management and aquaculture resulted in 3.8 (2.5–5.8) TgN yr^–1 (average 2007–2016) (robust evidence, high agreement) (Table 5.3; Winiwarter et al., 2018; FAO, 2019; Janssens-Maenhout et al., 2019; Tian et al., 2020). Observations from field-measurements (Song et al., 2018), inventories (Wang et al., 2020) and atmospheric inversions (Thompson et al., 2019) further corroborate the assessment of SRCCL that there is a non-linear relationship between N₂O emissions and nitrogen input, implying an increasing fraction of fertilizer lost as N₂O with larger fertilizer excess (medium evidence, high agreement). Several studies using complementary methods indicate that agricultural N₂O emissions have increased by more than 45% since the 1980s (high confidence) (Figure 5.16 and Table 5.3; Davidson, 2009; Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al., 2020), mainly due to the increased use of nitrogen fertilizer and manure. N₂O emissions from aquaculture are among the fastest rising contributors of N₂O emissions, but their overall magnitude is still small in the overall N₂O budget (Tian et al., 2020).

The principal non-agricultural anthropogenic sources of N₂O are industry, specifically chemical processing, wastewater, and the combustion of fossil fuels (Table 5.3). Industrial emissions of N₂O mainly due to nitric and adipic acid production have decreased in North America and Europe since the widespread installation of abatement technologies in the 1990s (Pérez-Ramίrez et al., 2003; Lee et al., 2011; Janssens-Maenhout et al., 2019). There is still considerable uncertainty in industrial emissions from other regions of the world with contrasting trends between inventories (Thompson et al., 2019). Globally, industrial emissions and emissions from fossil fuel combustion by stationary sources, such as power plants, as well as smaller emissions from mobile sources (e.g., road transport and aviation) have remained nearly constant between the 1980s and 2007–2016 (medium evidence, medium agreement) (Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al., 2020). Wastewater N₂O emissions, including those from domestic and industrial sources, have increased from 0.2 (0.1–0.3) TgN yr^–1 to 0.35 (0.2–0.5) TgN yr^–1 between the 1980s and 2007–2016 (Tian et al., 2020).

Biomass burning from crop residue burning, grassland, savannah and forest fires, as well as biomass burnt in household stoves, releases N₂O during the combustion of organic matter. Updated inventories since AR5 (WGI, Section 6.4.3) result in a lower range of the decadal mean emissions of 0.6 (0.5–0.8) TgN yr^–1 (van der Werf et al., 2017; Tian et al., 2020). The attribution of grassland, savannah or forest fires to natural or anthropogenic origins is uncertain, preventing a separation of the biomass burning source into natural and anthropogenic.

Atmospheric deposition of anthropogenic N on oceans can stimulate marine productivity and influence ocean emissions of N₂O. New ocean model analyses since AR5 (WGI, 6.4.3), suggest a relatively modest global potential impact of 0.01–0.32 TgN yr^–1 (pre-industrial to present-day) equivalent to 0.5–3.3% of the global ocean N₂O source (Suntharalingam et al., 2012; Jickells et al., 2017; Landolfi et al., 2017). However, larger proportionate impacts are predicted in nitrogen-limited coastal and inland waters downwind of continental pollution outflow, such as the Northern Indian Ocean (Jickells et al., 2017; Suntharalingam et al., 2019).

Inland waters and estuaries are generally sources of N₂O as a result of nitrification and denitrification of dissolved inorganic nitrogen, however, they can serve as N₂O sinks in specific conditions (Webb et al., 2019). Since AR5 (WGI, 6.4.3), improved emissions factors, including their spatio-temporal scaling, and consideration of transport within the aquatic system allows for better constraint of these emissions (Murray et al., 2015; Hu et al., 2016; Lauerwald et al., 2019; Maavara et al., 2019; Kortelainen et al., 2020; Yao et al., 2020). Despite uncertainties because of the side effects of canals and reservoirs on nutrient cycling, these advances permit attribution of a fraction of inland water N₂O emissions to anthropogenic sources (Tian et al., 2020), which contributes to the increased anthropogenic share of the global N₂O source in this report compared to AR5 (Ciais et al., 2013). As an indirect consequence of agricultural nitrogen use and waste-water treatment, the anthropogenic emissions from inland waters have increased by about a quarter (0.1 TgN yr^–1) between the 1980s and 2007–2016 (Tian et al., 2020).

The synthesis of bottom-up estimates of N₂O sources (Sections 5.2.3.2–5.2.3.4 and Figure 5.17) yields a global source of 17.0 (12.2 to 23.5) TgN yr^–1 for the years 2007–2016 (Table 5.3). This estimate is comparable to AR5, but the uncertainty range has been reduced primarily due to improved estimates of ocean and anthropogenic N₂O sources. Since AR5 (WGI, Section 6.4.3), improved capacity to estimate N₂O sources from atmospheric N₂O measurements by inverting models of atmospheric transport provides a new and independent constraint for the global N₂O budget (Saikawa et al., 2014; Thompson et al., 2019; Tian et al., 2020). The decadal mean source derived from these inversions is remarkably consistent with the bottom-up global N₂O budget for the same period, however, the split between land and ocean sources based on atmospheric inversions is less constrained, yielding a smaller land source of 11.3 (10.2 to 13.2) TgN yr^–1 and a larger ocean source of 5.7 (3.4 to 7.2) TgN yr^–1, respectively, compared to bottom-up estimates.

Supported by multiple studies and extensive observational evidence (Sections 5.2.3.2–5.2.3.4 and Figure 5.17), anthropogenic emissions contributed about 40% (7.3; uncertainty range: 4.2 to 11.4 TgN yr^–1) to the total N₂O source in 2007–2016 (high confidence). This estimate is larger than in AR5 (WGI, 6.4.3) due to a larger estimated effect of nitrogen deposition on soil N₂O emissions and the explicit consideration of the role of anthropogenic nitrogen in determining inland water and estuary emissions.

Based on bottom-up estimates, anthropogenic emissions from agricultural nitrogen use, industry and other indirect effects have increased by 1.7 (1.0 to 2.7) TgN yr^–1 between the decades 1980–1989 and 2007–2016, and are the primary cause of the increase in the total N₂O source (high confidence). Atmospheric inversions indicate that changes in surface emissions, rather than in the atmospheric transport or sink of N₂O, are the cause for the increased atmospheric growth rate of N₂O (robust evidence, high agreement) (Thompson et al., 2019). However, the increase of 1.6 (1.4 to 1.7) TgN yr^–1 in global emissions between 2000–2005 and 2010–2015 based on atmospheric inversions is somewhat larger than bottom-up estimates over the same period, primarily because of differences in the estimates of land-based emissions.

The total influence of anthropogenic greenhouse gases (GHGs) on the Earth’s radiative balance is driven by the combined effect of those gases, and the three most important – carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O) – were discussed in the previous sections. This section compares the balance of the sources and sinks of these three gases and their regional net flux contributions to the radiative forcing. CO₂ has multiple residence times in the atmosphere – from one year to many thousands of years (Box 6.1 in Ciais et al., 2013) – and N₂O has a mean lifetime of 116 years. They are both long-lived GHGs, while CH₄ has a lifetime of 9.1 years and is considered a short-lived GHG (see Chapter 2 for lifetime of GHGs, Chapter 6 for CH₄ chemical lifetime, and Chapter 7 for effective radiative forcing of all GHGs).

The surface ocean has absorbed a quarter of all anthropogenic CO₂ emissions, mainly through physical–chemical processes (McKinley et al., 2016; Gruber et al., 2019b; Friedlingstein et al., 2020). Once dissolved in seawater, CO₂ reacts with water and forms carbonic acid, which in turn dissociates, leading to a decrease in the concentration of carbonate (CO₃^–2) ions, and increasing both bicarbonate (HCO₃^–) and hydrogen (H⁺) ion concentration. This process has caused a shift in the carbonate chemistry towards a less basic state, commonly referred to as ‘ocean acidification’ (Caldeira and Wickett, 2003; Orr et al., 2005; Doney et al., 2009). Although the societal concern regarding ocean acidification is relatively recent (about the last 20 years), the physical–chemical basis for the ocean absorption (sink) of atmospheric CO₂ has been discussed much earlier by Revelle and Suess (1957). The AR5 and SROCC assessments were of robust evidence that the H⁺ion concentration is increasing in the surface ocean, thereby reducing seawater pH (= -log [H⁺]) (Section 2.3.4.1; Orr et al., 2005; Feely et al., 2009; Ciais et al., 2013; Bindoff et al., 2019), and there is high confidence that ocean acidification is impacting marine organisms (Bindoff et al., 2019).

Ocean oxygen decline, or deoxygenation, is driven by changes in ocean ventilation and solubility (Bindoff et al., 2019). It is virtually certain that anthropogenic forcing has made a substantial contribution to the ocean heat content increase over the historical period (Bindoff et al., 2019; IPCC, 2019c; Chapter 9, Section 2.3.3.1), strengthening upper water column stratification. Ocean warming decreases the solubility of dissolved oxygen in seawater, and it contributes to about 15% of the dissolved oxygen decrease in the oceans according to estimates based on solubility and the recent SROCC assessment (medium confidence), especially in sub-surface waters, between 100–600 m depth (Helm et al., 2011; Schmidtko et al., 2017; Breitburg et al., 2018; Oschlies et al., 2018; SROCC, Section 5.3.1). Stratification reduces the ventilation flux into the ocean interior, contributing to most of the remaining ocean deoxygenation (Schmidtko et al., 2017; Breitburg et al., 2018; Section 3.6.2). Deoxygenation may enhance emissions of nitrous oxide, especially from oxygen minimum zones (OMZs) or hypoxic coastal areas (Breitburg et al., 2018; Oschlies et al., 2018). Since SROCC (Bindoff et al., 2019), CMIP6 model simulation results agree with the reported 2% loss (4.8 ± 2.1 Pmoles O₂) in total dissolved oxygen in the upper ocean layer (100–600 m) for the 1970–2010 period (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017; Kwiatkowski et al., 2020; Section 2.3.4.2). The response of marine organisms to the coupled effects of ocean warming, acidification and deoxygenation occur at different metabolic levels on different groups, and include respiratory stress and reduction of thermal tolerance (Gruber, 2011; Bindoff et al., 2019; IPCC, 2019c; Kawahata et al., 2019). An assessment of these effects on marine biota is found in WGII AR6 Chapter 2.

Since the beginning of the industrial period in the mid-19th century, coral δ¹¹B-derived ocean pH has decreased by 0.06–0.24 pH unit in the South China Sea (Liu et al., 2014; Wei et al., 2015) and 0.12 pH unit in the south-west Pacific (H.C. Wu et al., 2018). Since the mid-20th century, a distinct feature of coral δ¹¹B records relates to ocean acidification trends, albeit having a wide range of values: 0.12–0.40 pH unit in the Great Barrier Reef (Wei et al., 2009; D’Olivo et al., 2015), 0.05–0.08 pH unit in the north-west Pacific (Shinjo et al., 2013) and 0.04–0.09 pH unit in the Atlantic Ocean (Goodkin et al., 2015; Fowell et al., 2018). Concurrent coral carbon isotopic ( δ¹³C) measurements infer ocean uptake of anthropogenic CO₂ from the combustion of fossil fuel, based on the lower abundance of¹³C in fossil fuel carbon. Western Pacific coral records show depleted δ¹³C trends since the late 19th century that are more prominent since the mid-20th century (high confidence) (Pelejeroet al., 2005; Wei et al., 2009; Shinjo et al., 2013; Liu et al., 2014; Kubota et al., 2017; H.C. Wu et al., 2018).

Overall, many of the records show a highly variable seawater pH underlaying strong imprints of internal climate variability (high confidence) and, in most instances, superimposed on a decreasing δ¹¹B trend that is indicative of anthropogenic ocean acidification in recent decades (medium confidence). The robustness of seawater pH reconstructions is currently limited by the uncertainty on the calibration of The δ¹¹B proxy in different tropical coral species.

In the subtropical open oceans, decreases in pH have been reported with avery likely rate range from –0.016 to –0.019 pH units per decade since 1980s, which equates to approximately 4 % increase in hydrogen ion concentration ([H⁺]) per decade. Accordingly, the saturation state Ω (=[Ca²⁺][CO₃^2-]/K_sp) of seawater with respect to calcium carbonate mineral aragonite has been declining at rates ranging from –0.07 to –0.12 per decade (González-Dávila et al., 2010; Feely et al., 2012; Bates et al., 2014; Takahashi et al., 2014; Ono et al., 2019; Bates and Johnson, 2020; Supplementary Material Table 5.SM.3). These rates are consistent with the rates expected from the transient equilibration with increasing atmospheric CO₂ concentrations, but the variability of rate in decadal time scale has also been detected with robust evidence (Ono et al., 2019; Bates and Johnson, 2020). In the tropical Pacific, its central and eastern upwelling zones exhibited a faster pH decline of –0.022 to –0.026 pH unit per decade due to increased upwelling of CO₂ -rich sub-surface waters in addition to anthropogenic CO₂ uptake (Sutton et al., 2014; Lauvset et al., 2015). By contrast, warm pools in the western tropical Pacific exhibited slower pH decline of –0.010 to –0.013 pH unit per decade (Supplementary Material Table 5.SM.3; Lauvsetet al., 2015; Ishii et al., 2020). Observational and modelling studies (Nakano et al., 2015; Ishii et al., 2020) consistently suggest that slower acidification in this region is attributable to the anthropogenic CO₂ taken up in the extratropics around a decade ago and transported to the tropics via shallow meridional overturning circulations.

Advances in observations and modelling for ocean physics and biogeochemistry and established knowledge of ocean carbonate chemistry show with very high confidence that anthropogenic CO₂ taken up into the ocean surface layer is further spreading into the ocean interior through ventilation processes, including vertical mixing, diffusion, subduction and meridional overturning circulations (Sections 2.3.3.5, 5.2.1.3 and 9.2.2.3; Sallée et al., 2012; Bopp et al., 2015; Nakano et al., 2015; Iudicone et al., 2016; Toyama et al., 2017; Pérez et al., 2018; Gruber et al., 2019b) and is causing acidification in the ocean interior. The net change in oxygen consumption by aerobic respiration of marine organisms further influences acidification by releasing CO₂ (Section 5.3.3.2; Chen et al., 2017; Breitburg et al., 2018; Robinson, 2019).

Observations over past decades of basin-wide and global syntheses of ocean interior carbon show that the extent of acidification due to anthropogenic CO₂ invasion tends to diminish with depth (very high confidence) (Section 5.2.1.3.3 and Figure 5.21; Woosley et al., 2016; Carter et al., 2017; Lauvset et al., 2020). The regions of deep convection such as subpolar North Atlantic and Southern Ocean present the deepest acidification detections below 2000 m (medium confidence). Mid-latitudinal zones within the subtropical cells and tropical regions present a relatively deep and shallow detection, respectively. A pH decrease has also been observed on the Antarctic continental shelf (Hauck et al., 2010; Williams et al., 2015). Acidification is also underway in the subsurface to intermediate layers of the Arctic Ocean due to the inflow of ventilated waters from the North Atlantic and the North Pacific (Qi et al., 2017; Ulfsbo et al., 2018).

A significant increase in acidification resulting from net metabolic CO₂ release coupled with ocean circulation changes has been shown with high confidence in large swathes of intermediate waters in the Pacific and Atlantic oceans (Dore et al., 2009; Byrne et al., 2010; Ríos et al., 2015; Chu et al., 2016; Carter et al., 2017; Lauvset et al., 2020). For example, ocean circulation contributes a pH change of –0.013 ± 0.013 to the overall observed change of –0.029 ± 0.014 for 1993–2013 at depths around 1000 m at 30°S–40°S in the South Atlantic ocean (Ríos et al., 2015). Long-term repeated observations in the North Pacific show a decline in dissolved oxygen (–4.0 μmol kg⁻¹ per decade at maximum) being sustained in the intermediate water since the 1980s (Takatani et al., 2012; Sasano et al., 2015). The amplification of acidification associated with the weakening ventilation is thought to have been occurring persistently. In contrast, for the North Pacific subtropical mode water, large decadal variability in pH and aragonite saturation state with amplitudes of about 0.02 and about 0.1, respectively, are superimposed on secular declining trends due to anthropogenic CO₂ invasion (Oka et al., 2019). This is associated with the variability in ventilation due to the approximately 50% variation in the formation volume of the mode water that is forced remotely by the Pacific Decadal Oscillation (Qiu et al., 2013; Oka et al., 2015).

In summary, ocean acidification is spreading into the ocean interior. Its rates at depths are controlled by the ventilation of the ocean interior as well as anthropogenic CO₂ uptake at the surface, thereby diminishing with depth (very high confidence) (Figure 5.21). Variability in ocean circulation modulates the trend of ocean acidification at depths through the changes in ventilation and their impacts on metabolic CO₂ content. However, the large knowledge gap around ventilation changes leads to low confidence in their impacts in many ocean regions (Sections 5.3.3.2; 9.2.2.3 and 9.3.2).

In oxygen-depleted waters, microbial processes (denitrification and anammox, i.e., anaerobic ammonium oxidation; Kuypers et al., 2005; Codispoti, 2007; Gruber and Galloway, 2008) remove fixed nitrogen, and when upwelled waters reach the photic zone, primary production becomes nitrogen-limited (Tyrrell and Lucas, 2002). However, in other oceanic regions, increased water-column stratification due to warming may reduce the amount of N₂O reaching the surface and thereby decrease N₂O flux to the atmosphere. Landolfi et al. (2017) suggest that, by 2100, under the RCP8.5 scenario, total N₂O production in the ocean may decline by 5% and N₂O emissions be reduced by 24% relative to the pre-industrial era due to decreased organic matter export and anthropogenic-driven changes in ocean circulation and atmospheric N₂O concentrations. Projected oxygen loss in the ocean is thought to result in an ocean-climate feedback through changes in the natural emissions of GHGs (low confidence).

In surface ocean, changes in the amplitude of seasonal variations in pH are also projected to occur with high confidence. ESMs in CMIP6 show +73 ± 12% increase in the amplitude of seasonal variation in hydrogen ion concentration ([H⁺]) but 10 ± 5% decrease in the seasonal variation in pH (= -log [H⁺]) from 1995–2014 to 2080–2099 under SSP5-8.5. The simultaneous amplification of [H⁺] and attenuation of pH seasonal cycles is counterintuitive but is the consequence of a greater increase in the annual mean [H⁺] due to anthropogenic CO₂ invasion than the corresponding increase in its seasonal amplitude. These changes are consistent with the amplification/attenuation of the seasonal variation of +81 ±16% for [H⁺] and –16 ± 7% for pH from 1990–1999 to 2090–2099 under RCP8.5 in CMIP5 (Kwiatkowski and Orr, 2018).

The signal of ocean acidification in surface ocean is large and is projected to emerge beyond the range of natural variability within the time scale of a decade in all ocean basins (Schlunegger et al., 2019). There is high agreement among modelling studies that the largest pH decline and large-scale undersaturation of aragonite in surface seawater start to occur first in polar oceans (Orr et al., 2005; Steinacher et al., 2009; Hurd et al., 2018; Jiang et al., 2019). Under SSP5-8.5, the largest surface pH decline, exceeding 0.45 between 1995–2014 and 2080–2099, occurs in the Arctic Ocean (Kwiatkowski et al., 2020). The freshwater input from sea ice melt is an additional factor leading to a faster decline of aragonite saturation level than expected from the anthropogenic CO₂ uptake (Yamamoto et al., 2012). The increase in riverine and glacial discharges that provide terrigenous carbon, nutrients and alkalinity as well as freshwater are the other factors modifying the rate of acidification in the Arctic Ocean. However, their impacts have been projected in a limited number of studies with extensive knowledge gaps and model simplifications leading to low confidence in their impacts (Terhaar et al., 2019; Hopwood et al., 2020). In the Southern Ocean, the aragonite undersaturation starts in the 2030s in RCP8.5, and the area that experiences aragonite undersaturation for at least one month per year by 2100 is projected to be more than 95%. Under RCP2.6, short periods (less than one month) of aragonite undersaturation are expected to be found in less than 2% of the area during this century (Sasse et al., 2015; Hauri et al., 2016; Negrete-García et al., 2019). These long term projections are modified at interannual time scales by large-scale climate modes (Ríos et al., 2015) such as the ENSO and the Southern Annular Mode (Conrad and Lovenduski, 2015). In other regions, acidification trends are influenced by a range of processes such as changes in ocean circulation, temperature, salinity, carbon cycling, and the structure of the marine ecosystem. As, at present, models do not resolve fine-scale variability of these processes, current projections do not fully capture the changes that the marine environment will experience in the future (Takeshita et al., 2015; Turi et al., 2016).

The coastal ocean, from the shoreline to the isobath of 200 m, is highly heterogeneous due to the complex interplay between physical, biogeochemical and anthropogenic factors (Gattuso et al., 1998; Chen and Borges, 2009; Dürr et al., 2011; Laruelle et al., 2014; McCormack et al., 2016). These areas, according to SROCC (Bindoff et al., 2019) are, with high confidence, already affected by ocean acidification and deoxygenation. This section assesses the drivers and spatial variability of acidification and deoxygenation based on new observations and data products.

Observations and data products including models (Astor et al., 2013; Bakker et al., 2016; Kosugi et al., 2016; Vargas et al., 2016; Laruelle et al., 2017, 2018; Orselli et al., 2018; Roobaert et al., 2019; Cai et al., 2020; H. Sun et al., 2020) confirm the strong spatial and temporal variability in the coastal ocean surface carbonate chemistry and sea-air CO₂ fluxes (high agreement, robust evidence). The anthropogenic CO₂ -induced acidification is either mitigated or enhanced through biological processes; primary production removes dissolved CO₂ from the surface, and respiration adds CO₂ and consumes oxygen in the subsurface layers. The relative intensity of these processes is controlled by natural or anthropogenic eutrophication. Other drivers of variability include biological community composition, freshwater input from rivers or melting ice, sea ice cover and calcium carbonate precipitation/dissolution dynamics, coastal upwelling and regional circulation, and seasonal surface cooling (Fransson et al., 2015, 2017; Feely et al., 2018; Roobaert et al., 2019; Cai et al., 2020; Hauri et al., 2020; Monteiro et al., 2020b; H. Sun et al., 2020). Near-shore surface waters are often supersaturated with CO₂, regardless of the latitude, especially in highly populated areas receiving substantial amounts of domestic and industrial sewage (Chen and Borges, 2009). Nevertheless, thermal or haline-stratified eutrophic coastal areas may act as net atmospheric CO₂ sinks (Chou et al., 2013; Cotovicz Jr. et al., 2015). Continental shelves, excluding near-shore areas, act as CO₂ sinks at a rate of 0.2 ± 0.02 PgC yr^–1 (Laruelle et al., 2014; Roobaert et al., 2019), considering ice-free areas only. Under increasing atmospheric CO₂ and eutrophication, such ecosystems would be more vulnerable to ecological and seawater chemistry changes, impacting the local economy.

Since SROCC (Bindoff et al., 2019), there is further evidence that anthropogenic eutrophication via continental runoff and atmospheric nutrient deposition, and ocean warming are very likely the main drivers of deoxygenation in coastal areas (Levin and Breitburg, 2015; Levin et al., 2015; Royer et al., 2016; Breitburg et al., 2018; Cocquempot et al., 2019; Fagundes et al., 2020; Limburg et al., 2020). Increasing intensity and frequency of wind-driven upwelling is responsible for longer and more intense coastal hypoxia, fuelled by organic matter degradation from primary production(medium to high agreement, medium evidence) (Rabalais et al., 2010; Bakun et al., 2015; Varela et al., 2015; Fennel and Testa, 2019; Limburg et al., 2020). Locally, submarine groundwater discharge may enhance the eutrophication state (low agreement, limited evidence, Luijendijk et al., 2020). Since AR5 (Ciais et al., 2013) and SROCC (Bindoff et al., 2019) new observations and model studies confirm the trends in increasing coastal hypoxia caused by eutrophication, ocean warming and changes in circulation (Claret et al., 2018; Dussin et al., 2019; Limburg et al., 2020), as well as the ubiquitous impacts on marine organisms and fisheries (AR6 WGII Chapter 3; Carstensen and Conley, 2019; Fennel and Testa, 2019; Osma et al., 2020). Following open ocean deoxygenation trends since the 1950s, more than 700 coastal regions are being reported as hypoxic (dissolved oxygen concentration <2 mg O₂L^–1) (Limburg et al., 2020). Additionally, deoxygenation or increasing severe hypoxic periods in coastal areas may enhance the sea-to-air fluxes of N₂O and CH₄ especially through microbial-mediated processes in the water column–sediment interface (medium agreement) (Middelburg and Levin, 2009; Naqvi et al., 2010; Farías et al., 2015; Limburg et al., 2020).

Observations and models predict an expansion and intensification of low-pH deep water intrusions for the north-east Pacific coastal upwelling area (high agreement, robust evidence) (Hauri et al., 2013; Feely et al., 2016; Cai et al., 2020). Areas such as the California Current System are naturally exposed to intrusions of low‐pH, high pCO_2sea deep waters from remineralization processes and anthropogenic CO₂ intrusion (Feely et al., 2008, 2010, 2018; Chan et al., 2019; Lilly et al., 2019; Cai et al., 2020).The eastern Pacific coastal upwelling displays seasonality in subsurface aragonite undersaturation as a consequence of the interplay between anthropogenic CO₂, respiration and intrusion of upwelling waters (Feely et al., 2008, 2010, 2016, 2018; Hauri et al., 2013; Vargas et al., 2016; Chan et al., 2019; Lilly et al., 2019). The coastal south-east Pacific upwelling combined with low-pH, low-alkalinity, organic matter-rich river inputs display extreme temporal variability in surface seawaterpCO₂ and low aragonite saturation (Vargas et al., 2016; Osma et al., 2020).

Many coastal tropical areas are under heavy anthropogenic eutrophication induced by the effluents from large cities, or receive large riverine inputs of freshwater, nutrients, and organic matter (such as Amazon, Mississippi, Orinoco, Congo, Mekong, or Changjiang rivers). Under strong eutrophication, often sub-surface and bottom waters present pH values lower than average surface open ocean (about 8.0) because increased respiration decreases pH (high agreement, robust evidence), despite a net atmospheric CO₂ sink in shallow and vertically stratified coastal areas (Koné et al., 2009; Wallace et al., 2014; Cotovicz Jr. et al., 2015, 2018; Fennel and Testa, 2019; Lowe et al., 2019; Section 5.3.5.1).

There is medium evidence from observations and models that the coastal north-western Antarctic Peninsula (Southern Ocean) will experience calcium carbonate undersaturation by 2060, considering that anthropogenic emissions reach an atmospheric CO₂ concentration of about 500 pm at that date (Lencina-Avila et al., 2018; Monteiro et al., 2020a). The synergies among warming, melt water, sea-air CO₂ equilibrium and circulation may, to some extent, offset the coastal ocean acidification trends in Antarctica (Henley et al., 2020). In the coastal western Arctic Ocean, there is increasingrobust evidence that ocean acidification is driven by sea-air CO₂ fluxes and sea-ice melt, and increasing intrusions since the 1990s of low-alkalinity Pacific water, lowering aragonite saturation state (Qi et al., 2017, 2020; Cross et al., 2018). The Bering Sea (north-eastern Pacific) shows decreasing trends in calcium carbonate saturation, associated to the increasing atmospheric CO₂ uptake combined with riverine freshwater and carbon inputs (high agreement, robust evidence) (Pilcher et al., 2019; H. Sun et al., 2020).

There is medium agreement (medium evidence) that simply reducing anthropogenic nutrient inputs may lead to less severe coastal hypoxic conditions, as observed in the coastal north-western Adriatic Sea (Djakovac et al., 2015). However, low-oxygen sediments may remain a long-term source of phosphorus and ammonium to the water column, and in this way fuelling primary production (Jokinen et al., 2018; Fennel and Testa, 2019; Limburg et al., 2020).

Our understanding of the various biological processes that affect the strength of the CO₂ fertilization effect on photosynthesis and its impact on carbon storage in vegetation and soils, (in particular regarding the limitations imposed by nitrogen and phosphorus availability), has developed since AR5 (WGI, Box 6.2). Based on consistent behaviour across all CMIP6 ESMs, there is high confidence that CO₂ fertilization of photosynthesis acts as an important negative feedback on anthropogenic climate change, by reducing the rate at which CO₂ accumulates in the atmosphere. Since AR5 (WGI, Box 6.2), an increasing number of CMIP6 ESMs account for nutrient cycles. The consistent results found in their model projections suggests with high confidence that limited nutrient availability will limit the CO₂ fertilization effect (Arora et al., 2020). The magnitude of the direct CO₂ effect on land carbon uptake, and its limitation by nutrients, remains uncertain.

In AR5 (WGI, Section 6.4.2) there was high agreement that CMIP5 ESMs project continued ocean CO₂ uptake through to 2100, with higher uptake corresponding to higher concentration or emissions pathways. There has been no significant change in the magnitude of the sensitivity of ocean carbon uptake to increasing atmospheric CO₂, or in the inter-model spread, between the CMIP5 and CMIP6 era (Arora et al., 2020). The analysis from emissions and concentration-driven CMIP5 model projections show that the ocean sink stops growing beyond 2050 across all emissions scenarios (Section 5.4.5.3). CMIP6 models also show a similar time evolution of global ocean CO₂ uptake to CMIP5 models over the 21st century (Figure 5.25) with decreasing net ocean CO₂ uptake ratio to anthropogenic CO₂ emissions under SSP5-8.5.

The projected weakening of ocean carbon uptake is driven by a combination of decreasing carbonate buffering capacity and warming, which are positive feedbacks under weak to no mitigation scenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), weakening ocean carbon uptake is driven by decreasing emissions (Cross-Chapter Box 5.3). The detailed understanding of carbonate chemistry in seawater that has accumulated over more than half a century (e.g., Revelle and Suess, 1957; Egleston et al., 2010), provides high confidence that the excess CO₂ dissolved in seawater leads to a non-linear reduction of the CO₂ buffering capacity, that is smaller dissolved inorganic carbon (DIC) increase with respect to pCO₂ increase along with the increase in cumulative ocean CO₂ uptake. Recent studies (Katavouta et al., 2018; Jiang et al., 2019; Arora et al., 2020; Rodgers et al., 2020) suggest with medium confidence that the decrease in the ocean CO₂ uptake ratio to anthropogenic CO₂ emissions, under low to no mitigation scenarios over the 21st century, is predominantly attributable to the ocean carbon-concentration feedback through the reduction of the seawater CO₂ buffering capacity, but with contributions from physical drivers such as warming and wind stress (medium confidence) and biological drivers (low confidence) (Sections 5.2.1.3.3 and 5.4.4).

Projected increases in ocean DIC due to anthropogenic CO₂ uptake amplify the sensitivity of carbonate system variables to perturbations of DIC in the surface ocean, for example via the amplitude of the seasonal cycle of pCO₂, which impacts the mean annual air–sea fluxes (Hauck et al., 2015; Fassbender et al., 2018; Landschützer et al., 2018; SROCC, Section 5.2.2.3). A larger amplification of the surface oceanpCO₂ seasonality occurs in the subtropics where pCO₂ seasonality is dominated by temperature seasonality, with the summer increase in the difference inpCO₂ between surface water and the overlying atmosphere reaching 3μatm per decade between 1990 and 2030 under RCP8.5 (Schlunegger et al., 2019; Rodgers et al., 2020). In contrast, the impact of biological production on the seasonal cycle of pCO₂ in summer in the Southern Ocean strengthens the drawdown of CO₂ (Hauck et al., 2015).

Overall, there is medium confidence on three outcomes in the ocean from projected CO₂ uptake under medium to high CO₂ concentration scenarios: (i) a weakening of the buffering capacity, which impacts the airborne fraction via the reduction of the ocean CO₂ buffering capacity due to cumulative ocean CO₂ uptake, which reduces the net ocean CO₂ uptake ratio to anthropogenic CO₂ emissions (Katavouta et al., 2018; Arora et al., 2020; Rodgers et al., 2020); (ii) an amplification of the seasonal cycle of CO₂ variables, which impacts both the ocean sink and ocean acidification (Hauck et al., 2015); (iii) a decrease in the aragonite and calcite saturation levels in the ocean, which negatively impacts the calcification rates of marine organisms (high confidence) and forms a negative feedback on the uptake of CO₂ (McNeil and Sasse, 2016) (Cross-Chapter Box 5.3).

Peat soils, where thick organic layers build up due to saturated and anoxic conditions, represent another possible source of carbon to the atmosphere. Peats could dry, and decompose or burn as a result of climate change in both high (Chaudhary et al., 2020) and tropical (Cobb et al., 2017) latitudes, and in combination with anthropogenic drainage of peatlands (Warren et al., 2017). Peat carbon dynamics are not included in the majority of CMIP6 ESMs. Soil microbial dynamics shift in response to temperature, giving rise to complex longer-term trophic effects that are more complex than the short-term sensitivity of decomposition to temperature. Such responses are observed in response to long-term warming experiments (Melillo et al., 2017). While most CMIP6 ESMs do not include microbial dynamics, simplified global soil models that do include such dynamics show greater uncertainty in projections of soil carbon changes, despite agreeing more closely with current observations, than the linear models used in most ESMs (Wieder et al., 2013; Guenet et al., 2018).

The permafrost region was a historic carbon sink over centuries to millennia (high confidence) (Loisel et al., 2014; Lindgren et al., 2018). Currently though, thawing soils due to anthropogenic warming are losing carbon from the decomposition of old frozen organic matter, as found via carbon 14 (¹⁴C) signature of respiration at sites undergoing rapid permafrost thaw (Hicks Pries et al., 2013), of dissolved organic carbon in rivers draining watersheds with permafrost thaw (Vonk et al., 2015; Wild et al., 2019), and of methane (CH₄) produced in thawing lakes (Walter Anthony et al., 2016).

The principal contribution to increasing global ocean carbon is the air–sea flux of CO₂, which changes the dissolved inorganic carbon (DIC) inventory (Section 5.4.2; Arora et al., 2020). The processes that influence the variability and trends of the ocean carbon–heat nexus are assessed in Cross-Chapter Box 5.3. Climate has three important impacts on the ocean uptake of anthropogenic CO₂ : (i) ocean warming reduces the solubility of CO₂, which increases pCO₂ and increases the stratification of the mixed layer, both acting as positive feedbacks weakening the ocean sink (Section 9.2.1 and Cross-Chapter Box 5.3; Arora et al., 2020); (ii) changing the temporal and spatial characteristics of wind stress and storms alters mixing – entrainment in, and across the bottom of, the mixed layer (Bronselaer et al., 2018); and (iii) warming and wind stress influence the large-scale meridional overturning circulation (MOC) circulation, which modifies the rate of ventilation, storage or outgassing of ocean carbon in the ocean interior (Section 5.2.3.1; Gruber et al., 2019b; Arora et al., 2020). The land-to-ocean riverine flux and the carbon burial in ocean sediments play a minor role (low confidence) (Arora et al., 2020). Based on high agreement of projections by coupled climate models, there is high confidence that the resultant climate–carbon cycle feedbacks are positive, but the extent of the ocean sink weakening is scenario dependent (Arora et al., 2020).

Regionally, the Southern Ocean is a major sink of anthropogenic CO₂ (Figure 5.8a), although challenges in modelling its circulation and Antarctic sea ice transport (Sections 3.4.1.2, 9.2.3.2 and 9.3.2) generate uncertainty in the response of its sink to future carbon–climate feedbacks. Increased freshwater input may cause a slowdown of the lower overturning circulation, leading to increased Southern Ocean biological carbon storage (Ito et al., 2015); alternatively, increased winds may intensify the overturning circulation, reducing the net CO₂ sink in the Southern Ocean (Bronselaer et al., 2018; Saunders et al., 2018). On centennial time scales, there is thus low confidence in the overall effect of intensifying winds in the Southern Ocean on CO₂ uptake.

While physical drivers control the present-day anthropogenic carbon sink, biological processes are responsible for the majority of the vertical gradient in DIC (natural carbon storage). A small fraction of the organic carbon fixed by primary production (PP) reaches the sea floor, where it can be stored in sediments on geological time scales, making the biological carbon pump (BCP) an important mechanism for very long-term CO₂ storage. Projected reductions in ocean ventilation (Section 9.2.1.4) would lengthen residence time and lead to DIC accumulating in the deep ocean due to organic carbon remineralization.

ESMs can be driven by anthropogenic CO₂ emissions (‘emissions-driven’ runs), in which case atmospheric CO₂ concentration is a predicted variable; or by prescribed time-varying atmospheric concentrations (‘concentration-driven’ runs). In concentration-driven runs, simulated land and ocean carbon sinks respond to the prescribed atmospheric CO₂ and resulting changes in climate, but do not feed back through changes in the atmospheric CO₂ concentration. Concentration-driven runs are used to diagnose the carbon emissions consistent with the Shared Socio-economic Pathways (SSPs) and other prescribed concentration scenarios (Section 5.5). In this subsection we specifically analyse results from concentration-driven ESM projections.

In summary, oceanic and terrestrial carbon sinks are projected to continue to grow with increasing atmospheric concentrations of CO₂, but the fraction of emissions taken up by land and ocean is expected to decline as the CO₂ concentration increases (high confidence). In the ensemble mean, ESMs suggest approximately equal global land and ocean carbon uptake for each of the SSP scenarios. However, the range of model projections is much larger for the land carbon sink. Despite the wide range of model responses, uncertainty in atmospheric CO₂ by 2100 is dominated by future anthropogenic emissions rather than carbon–climate feedbacks (high confidence).

The applicability of the linear feedback framework (Section 5.4.5.5) suggests that large-scale biogeochemical feedbacks are approximately linear in the forcing from changes in CO₂ and climate. Nevertheless, regionally the biosphere is known to be capable of producing abrupt changes or even ‘tipping points’ (Higgins and Scheiter, 2012; Lasslop et al., 2016). Abrupt change is defined as a change in the system that is substantially faster than the typical rate of the changes in its history (Section 1.4.5). A related matter is a tipping point: a critical threshold beyond which a system reorganizes, often abruptly and/or irreversibly. Possible abrupt changes in the Earth system include those related to ecosystems and biogeochemistry (Lenton et al., 2008; Steffen et al., 2018): tropical and boreal forest dieback; and release of greenhouse gases (GHGs) from permafrost and methane clathrates (Table 5.6). In this section we therefore focus on estimating upper limits on the possible impact of abrupt changes on the evolution of atmospheric GHGs out to 2100, for comparison to the impact of direct anthropogenic emissions.

There is large uncertainty in release of GHGs from permafrost in the 21st century. The largest of these estimates implies tens to hundreds of gigatons of carbon released in the form of CO₂ (Box 5.1) and CH₄ emissions up to 100 TgCH₄yr^–1 (Box 5.1). A carbon dioxide release of such magnitude would lead to an increase in the CO₂ accumulation rate in the atmosphere of ≤1 ppm yr^–1. These emissions develop at a multi-decadal time scale. Assuming a CH₄ lifetime in the atmosphere of the order of 10 years and the associated feedback parameter of 1.34 ± 0.04 (Section 6.2.2.1), this would increase the atmospheric CH₄ content by about 500 ppb over the century, corresponding to a rate of ≤10 ppb yr^–1. Irrespective of its origin, additional CH₄ accumulation of such a magnitude is not expected to modify the temperature response to anthropogenic emissions by more than a few tenths of a °C (Gedney et al., 2004; Eliseev et al., 2008; Denisov et al., 2013). Emissions from permafrost thawing are assessed in Box 5.1.

Based on the evidence presented in this section, we conclude that abrupt changes and tipping points in the biogeochemical cycles lead to additional uncertainty in 21st century GHG concentrations changes. However, these are very likely to be small compared to the uncertainty associated with future anthropogenic emissions (high confidence).

Figure 5.30 shows carbon cycle changes to 2300 under three SSP scenarios with long-term extensions: SSP5-8.5, SSP5-3.4-overshoot, and SSP1-2.6, for four CMIP6 ESMs and one EMIC. Under all three scenarios, all five models project a reversal of the terrestrial carbon cycle from a sink to a source. However, the reasons for these reversals under very high emissions and low/negative emissions are very different. Under the SSP5-8.5 scenario, the terrestrial carbon–climate feedback is projected to strengthen, while the carbon-concentration feedbacks weaken after emissions peak at 2100, which together drives the land to become a net carbon source after 2100 (Tokarska et al., 2016). The difference in both timing and magnitude of this transition across the ensemble, leads to an assessment of medium confidence in the likelihood and low confidence in the timing and strength, of the land transitioning from a net sink to a net source under such a scenario. Based on high agreement across all available models, we assess with high confidence that the ocean sink strength would weaken but not reverse under a long-term high emissions scenario. In the SSP5-3.4-overshoot scenario, both the terrestrial and ocean reservoirs act as transient carbon sources during the overshoot period, when net anthropogenic CO₂ emissions are negative and CO₂ concentrations are falling, and then revert to near-zero (land) or weak sink (ocean) fluxes after stabilization of atmospheric CO₂. The SSP1-2.6 scenario, characterized by lower peak CO₂ concentrations, a smaller overshoot, and much less carbon loss from land-use change, shows instead a relaxation towards a neutral biosphere on land, and a sustained weak sink in the ocean (see also Section 5.6.2.2.1.2).

Since AR5, new studies have further expanded the evidence base for estimating the value of TCRE. These studies rely on ESMs or EMICs, observational constraints and concepts of attributable warming, or theoretically derived equations (see Table 5.7 for an overview). Several studies have endeavoured to partition the uncertainty in the value of TCRE into constituent sources. For example, TCRE can be decomposed into terms of TCR and the airborne fraction of anthropogenic CO₂ emissions over time (Allen et al., 2009; Matthews et al., 2009). These two terms are assessed individually (see Section 5.4 and Chapter 7, respectively) and allow the integration of evidence assessed elsewhere in the report into the assessment of TCRE (Section 5.5.1.4). Further studies use a variety of methods, including analysing the outputs from CMIP5 (R.G. Williams et al., 2017b) or CMIP6 (Arora et al., 2020; Jones and Friedlingstein, 2020), conducting perturbed parameter experiments with a single model (MacDougall et al., 2017), Monte-Carlo methods applied to a simple climate model (Spafford and Macdougall, 2020), or observations and estimates of the contribution of CO₂ and non-CO₂ forcers (Matthews et al., 2021). All of the studies agree that uncertainty in climate sensitivity – either equilibrium climate sensitivity (ECS) or transient climate response (TCR) – is among the most important contribution to uncertainty in TCRE, with uncertainty in the strength of the land carbon feedback and ocean heat uptake or ventilation having also been identified as crucial to uncertainty in TCRE (Matthews et al., 2009; Gillett et al., 2013; Ehlert et al., 2017; MacDougall et al., 2017; R.G. Williams et al., 2017a, 2020; Katavouta et al., 2019; Arora et al., 2020; Jones and Friedlingstein, 2020; Spafford and Macdougall, 2020). Finally, internal variability has been shown to affect the maximum accuracy of TCRE estimates by ±0.1°C per 1000 PgC (5–95% range; Tokarska et al., 2020).

In the past 60 years, the ocean has taken up and stored 23 ± 5% of anthropogenic carbon emissions (medium confidence) (Section 5.2.1.3) as well as more than 90% of the heat that has accumulated in the Earth system (referred to as excess heat) since the 1970s (Sections 7.2.2, 9.2.2 and 9.2.3, and Box 7.2; Frölicher et al., 2015; Talley et al., 2016; Gruber et al., 2019b; Hauck et al., 2020). The interplay between heat and CO₂ uptake by the ocean has played a major role in slowing the rate of global warming, and also provides a first order influence in determining the unique properties of a metric of the coupled climate–carbon cycle response – transient climate response to cumulative CO₂ emissions (TCRE) – which is critical to setting the future remaining carbon emissions budget (Sections 5.5.1.3 and 5.5.4). This role of the ocean in the uptake of heat and anthropogenic CO₂ and related feedbacks is what we term the ‘ocean carbon–heat nexus’. The ocean processes behind this nexus are important in shaping and understanding the near-linear relationship between cumulative CO₂ emissions and global warming (TCRE) as well as the uncertainties in future projections of TCRE properties (Zickfeld et al., 2016; Bronselaer and Zanna, 2020; Jones and Friedlingstein, 2020), its path independence (MacDougall, 2017), and the warming commitment after cessation of greenhouse gas emissions – the zero emissions commitment (ZEC; Section 5.5.2; Zickfeld et al., 2016; Ehlert and Zickfeld, 2017). In this box, we assess the role of the ocean and its physical and chemical thermodynamic processes that shape these striking characteristics.

The air–sea flux of heat and all gases across the ocean interface is driven by a common set of complex and turbulent diffusion and mixing processes that are difficult to observe (Sections 5.2.1.3 and 9.2.1.2; Wanninkhof et al., 2009; Wanninkhof, 2014; Cronin et al., 2019; Watson et al., 2020). These processes are typically simplified into widely verified expressions that link the flux to wind stress, the solubility and the gradient across the air–sea interface (medium confidence). Because the ocean has a higher heat capacity than the atmosphere (the heat capacity of the upper 100 m of the ocean is about 30 times larger than the heat capacity of the atmosphere), the partitioning of heat between the atmosphere and the ocean is primarily influenced by the temperature differences between air and seawater. Similarly, the unique seawater carbonate buffering capacity enables CO₂ to be stored in the ocean as dissolved salts, rather than just as dissolved gas; this increases the capacity of seawater to store CO₂ by two orders of magnitude beyond the solubility of CO₂ gas and approximates the partitioning ratio of heat between the atmosphere and the ocean (Section 9.2.2.1; Zeebe and Wolf-Gladrow, 2009; Bronselaer and Zanna, 2020). The role of the biological carbon pump in influencing the ocean sink of anthropogenic carbon into the ocean interior is assessed to be minimal during the historical period, but this may change, particularly in regional contexts, by 2100 (medium confidence) (Laufkötter et al., 2015; Kwiatkowski et al., 2020). Its role is important in the natural or pre-industrial carbon cycle (medium confidence) (Henson et al., 2016).

Under climate change, the buffering capacity of the ocean decreases (increasing Revelle Factor), which reflects a decreasing capacity for the ocean to take up additional anthropogenic CO₂ and store it in the dissolved inorganic carbon reservoir (Egleston et al., 2010). In contrast to CO₂, there is no physical limitation that would reduce the ability of surface ocean temperature to equilibrate with the atmospheric temperature. However, both carbon and heat fluxes depend on air–sea heat fluxes that in turn depend on gradients of characteristics at the air–sea interface. These gradients at the air–sea interface respond to ocean dynamics, such as the volume of the surface mixed-layer that equilibrates with the atmosphere, and ocean circulation that can flush the surface layer with water masses that have not equilibrated with the atmosphere for a long time. Limited recent evidence suggests that the effect of small-scale dynamics absent in climate and Earth system models might be locally important (Bachman and Klocker, 2020). In summary, changes in heat and carbon uptake by the ocean rely on a combination of unique chemical and shared physical processes, any of which have the potential to disrupt the coherence of heat and CO₂ change in the ocean.

The role of the large-scale circulation in shaping these fluxes is to: (i) flush the ocean surface layer with deep waters that are relatively cold and with weak or no anthropogenic CO₂ and heat content because they have been isolated from the atmosphere for centuries; and (ii) transport the anthropogenic CO₂ and heat at depth, away from the atmosphere (Frölicher et al., 2015; Marshall et al., 2015; Armour et al., 2016). For instance, in the Southern Ocean, upwelled water masses take up a large amount of anthropogenic CO₂ and heat (Cross-Chapter Box 5.3, Figure 1), which are then exported northward by the circulation to be stored at depth in the Southern Hemisphere subtropical gyres (Cross-Chapter Box 5.3, Figure 1; Figure 9.7). In the North Atlantic, the signature of the Atlantic meridional overturning circulation (AMOC) is also clearly visible, with large amounts of heat and carbon being stored beneath the North Atlantic subtropical gyre at 1 km depth (Cross-Chapter Box 5.3, Figure 1). In summary, the net air–sea fluxes of anthropogenic CO₂ and heat depend on large-scale circulation, which is associated with upper ocean stratification, mixed-layer depth, and water-mass formation, transport and mixing (Sections 9.1–9.3).

Future projections of the ocean carbon–heat nexus in the second half of the 21st century, particularly those under weak or no mitigation scenarios, are characterized by the strengthening of the two largest positive feedbacks: weakening surface ocean CO₂ buffering capacity (increasing Revelle Factor) and warming that further reduces CO₂ solubility and strengthens ocean stratification, which reduces exchange between the ocean surface and interior (Jiang et al., 2019; Bronselaer and Zanna, 2020). These are offset by a growing but scenario-dependent negative feedback from increasing carbon and heat air–sea fluxes towards the ocean, due to increased atmospheric temperature and CO₂ concentrations (Talley et al., 2016; Jiang et al., 2019; McKinley et al., 2020). The Southern Ocean in particular is one of the regions where the projected feedback can be largest and where inter-model differences are strongest (Roy et al., 2011; Frölicher et al., 2015; Hewitt et al., 2016; Mongwe et al., 2018). These projected trends in ocean carbonate chemistry (Section 5.4.2), together with surface ocean warming (Section 9.2.1.1), explain the slow down and long-term reduction of the ocean sink for anthropogenic CO₂ even as emissions continue to rise beyond 2050 under weak-to-no-mitigation scenarios (Figures 2.7.1 and 5.25, and Technical Summary TS Box 7). Projected change in the North Atlantic and Southern Ocean overturning circulation also impact air–sea fluxes of heat and carbon. The very likely decline in AMOC in the 21st century for all shared socio-economic pathways (SSP) scenarios (Section 9.2.3.1) tends to reduce heat and carbon uptake, resulting in a positive feedback. In contrast, in the Southern Ocean, the future 21st century projected increase in upper ocean overturning circulation (low confidence) – due to increasing wind forcing projected for all scenarios, except those with large mitigation (SSP1-2.6) – produces a negative feedback, with increasing heat and carbon uptake and storage despite the increasing stratification and outgassing of natural CO₂ in the upwelling zone (Sections 9.2.3.2 and 5.2.1.3).

In summary, a combination of unique chemical properties of seawater carbonate combined with shared physical ocean processes explain the coherence and scaling in the uptake and storage of both CO₂ and heat in the ocean, which is the basis for the carbon–heat nexus (high confidence). In this way, the processes of the ocean carbon-heat nexus help understand the quasi-linear and path independence of properties of TCRE, which forms the basis for the zero emissions commitment (ZEC; Section 5.5) (medium confidence). Future projections under low or no mitigation indicate with high confidence that carbon chemistry and warming will strengthen the positive feedback to climate change by reducing ocean carbon uptake, and medium confidence that ocean circulation may partially compensate that positive feedback by slightly increasing anthropogenic carbon storage. Increasing ocean warming and stratification may decrease exchanges between the surface and subsurface ocean, which could reduce the path independence of TCRE, though this effect can be partially counterbalanced regionally by increasing circulation associated with increasing winds (low confidence).

The AR6 Glossary (Annex VII) defines remaining carbon budgets as the maximum amount of cumulative net global anthropogenic CO₂ emissions expressed from a recent specified date that would result in limiting global warming to a given level with a given probability, taking into account the effect of other anthropogenic climate forcers, consistent with the definition used inSR1.5 (Rogelj et al., 2018b). Studies, however, apply a variety of definitions that result in published remaining carbon budget estimates informing to cumulative emissions at the time when global-mean temperature increase would reach, exceed, avoid, or peak at a given warming level with a given probability (M. Collins et al., 2013; T.F. Stocker et al., 2013; Clarke et al., 2014; Friedlingstein et al., 2014a; IPCC, 2014; Rogelj et al., 2016; Millar et al., 2017b).

The impact of non-CO₂ emissions on remaining carbon budgets is assessed with emulators (Meinshausen et al., 2009; Millar et al., 2017b; Gasser et al., 2018; Goodwin et al., 2018; Rogelj et al., 2018b; C.J. Smith et al., 2018; Matthews et al., 2021) that incorporate synthesized climate and carbon-cycle knowledge (Cross-Chapter Box 7.1). The estimated implied non-CO₂ warming can subsequently be applied to reduce the remaining allowable warming for estimating the remaining carbon budget (Figure 5.31; Rogelj et al., 2018b, 2019). Alternative methods estimate the non-CO₂ fraction of total anthropogenic forcing (Matthews et al., 2021), or do not correct for non-CO₂ warming directly. The latter methods instead consider CO₂ and non-CO₂ warming together to define a CO₂ -forcing equivalent carbon budget from which eventual non-CO₂ contributions expressed in CO₂ -forcing-equivalent emissions have to be subtracted to obtain a remaining carbon budget (Jenkins et al., 2018; Matthews et al., 2020). These studies also use emulators to invert a specified evolution of non-CO₂ forcing to a corresponding amount of equivalent CO₂ emissions (Matthews et al., 2020), or alternatively use empirical relationships linking changes in non-CO₂ greenhouse gas emissions to warming (Cain et al., 2019). Methods to express non-CO₂ emissions in CO₂ equivalence are assessed in Section 7.6, yet their applicability and related uncertainties for remaining carbon budgets have not yet been covered in-depth in the literature.

For historical warming, SR1.5 used GSAT increase between 1850–1900 and 2006–2015 of 0.97°C as its main starting point, while also providing values for other temperature metrics. Remaining carbon budgets were expressed starting from 1 January 2018 by accounting for historical emissions emitted from 1 January 2011 until the end of 2017. AR6 uses anthropogenic (human-induced) warming until the 2010–2019 period, which is assessed at the 0.8-1.3°C range, with a best estimate of 1.07°C (Table 3.1), and subsequently accounts for historical emissions from 1 January 2015 until the end of 2019 to express remaining carbon budget estimates from 1 January 2020 onwards. The human-induced warming between the 1850–1900 and 2006–2015 periods used in SR1.5 was assessed by AR6 at 0.97°C (Table 3.1). In a like-with-like comparison, the combined effect of data and methodological updates in historical warming estimates thus results in no shift in estimated remaining carbon budgets between SR1.5 and AR6. However, the emissions of the years passed since SR1.5 reduce the remaining carbon budget by about 85 GtCO₂. Note that AR6 also updated its GSAT assessment for total warming between the 1850–1900 and 2006–2015 periods, reporting 0.94°C of warming. On a like-with-like basis, this would have resulted in slightly larger remaining carbon budgets compared to SR1.5 (Cross-Chapter Box 2.3).

Carbon dioxide removal (CDR) refers to anthropogenic activities that seek to remove CO₂ from the atmosphere and durably store it in geological, terrestrial or ocean reservoirs, or in products (Glossary). CO₂ is removed from the atmosphere by enhancing biological or geochemical carbon sinks or by direct capture of CO₂ from air and storage. Solar radiation modification (SRM) refers to the intentional, planetary-scale modification of the Earth’s radiative budget with the aim of limiting global warming. Most proposed SRM methods involve reducing the amount of incoming solar radiation reaching the surface, but others also act on the longwave radiation budget by reducing optical thickness and cloud lifetime (Glossary). SRM does not fall within the IPCC definitions of mitigation and adaptation (Glossary). CDR and SRM are referred to as ‘geoengineering’ in some of the literature, and are considered separately in this report.

Coastal wetlands and seagrass meadows store significant amounts of carbon and are among the most productive ecosystems per unit area (Griscom et al., 2017, 2020; Ortega et al., 2019; Serrano et al., 2019). These rates could be reduced in the future, since these habitats are vulnerable to changing conditions, such as temperature, salinity, sediment supply, storm severity and continued coastal development (Bindoff et al., 2019; NASEM, 2019). These ecosystems are under threat from anthropogenic conversion and degradation and are being lost at rates between 0.7% and 7% per annum with consequent CO₂ emissions (e.g., Atwood et al., 2017; Howard et al., 2017; Hamilton and Friess, 2018; Sasmito et al., 2019). Although sea level rise might lead to greater carbon sequestration in coastal wetlands (Rogers et al., 2019), there is high confidence that the frequency and intensity of marine heatwaves will increase (Cross-Chapter Box 9.1; Frölicher and Laufkötter, 2018; Laufkötter et al., 2020),which poses a more immediate threat to the integrity of coastal carbon stocks (Smale et al., 2019). Blue carbon restoration seeks to increase the rate of carbon sequestration, although restoration may be challenging, because of ongoing use of coastal land for human settlement, conversion to agriculture and aquaculture, shoreline hardening and port development.

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Global mean surface temperature (GMST) is a key indicator of the changing state of the climate system. Earth’s mean temperature history during the current geological era (Cenozoic, beginning 66 Ma (66 million years ago)) can be broadly characterized as follows (Cross-Chapter Box 2.1, Figure 1): (i) transient warming during the first 15 Myr (15 million years) of the Cenozoic, punctuated by the Paleocene–Eocene Thermal Maximum; (ii) a long-term cooling over tens of millions of years beginning around 50 Ma, driven by (among other factors) the slow drift of tectonic plates, which drove mountain building, erosion and volcanism, and reconfigured ocean passages, all of which ultimately moved carbon from the atmosphere to other reservoirs and led to the development of the Antarctic Ice Sheet (AIS) about 35–30 Ma; (iii) the intensification of cooling by climate feedbacks involving interactions among tectonics, ice albedo, ocean circulation, land cover and greenhouse gases, causing ice sheets to develop in the Northern Hemisphere (NH) by about 3 Ma; (iv) glacial-interglacial fluctuations paced by slow changes in Earth’s astronomical configuration (orbital forcing) and modulated by changes in the global carbon cycle and ice sheets on time scales of tens to hundreds of thousands of years, with particular prominence during the last 1 Myr; (v) a transition with both gradual and abrupt shifts from the Last Glacial Maximum to the present interglacial epoch (Holocene), with sporadic ice-sheet breakup disrupting ocean circulation; (vi) continued warming followed by minor cooling following the mid-Holocene, with superposed centennial- to decadal-scale fluctuations caused by volcanic activity, among other factors; (vii) recent warming related to the build-up of anthropogenic greenhouse gases (Sections 2.2.3 and 3.3.1).

This section assesses the magnitude and rates of changes in both natural and anthropogenically mediated climate drivers over a range of time scales. First, changes in insolation (orbital and solar; Section 2.2.1), and volcanic stratospheric aerosol (Section 2.2.2) are assessed. Next, well-mixed greenhouse gases (GHGs; CO₂, N₂O and CH₄) are covered in Section 2.2.3, with climate feedbacks and other processes involved in the carbon cycle assessed in Chapter 5. The section continues with the assessment of changes in halogenated GHGs (Section 2.2.4), stratospheric water vapour, stratospheric and tropospheric ozone (Section 2.2.5), and tropospheric aerosols (Section 2.2.6). Short-lived climate forcers (SLCFs), their precursor emissions and key processes are assessed in more detail in Chapter 6. Section 2.2.7 assesses the effect of historical land cover change on climate, including biophysical and biogeochemical processes. Section 2.2.8 summarizes the changes in the Earth’s energy balance since 1750 using the comprehensive assessment of effective radiative forcing (ERF) performed in Section 7.3. For some SLCFs with insufficient spatial or temporal observational coverage, ERFs are based on model estimates, but also reported here for completeness and context. Tabulated global mixing ratios of all well-mixed GHGs and ERFs from 1750–2019 are provided in Annex III.

In this section and for calculation of ERF, surface global averages are determined from measurements representative of the well-mixed lower troposphere. Global averages that include sites subject to significant anthropogenic activities or influenced by strong regional biospheric emissions are typically larger than those from remote sites, and require weighting accordingly (Table 2.2). This section focusses on global mean mixing ratios estimated from networks with global spatial coverage, and updated from the CMIP6 historical dataset (Meinshausen et al., 2017) for periods prior to the existence of global networks.

Other radiatively important gases with predominantly anthropogenic sources also continue to increase in abundance. SF₆, used in electrical distribution systems, magnesium production, and semi-conductor manufacturing, increased from 7.3 ppt in 2011 to 10.0 ppt in 2019 (+36%). Alternatives to SF₆ or SF₆-free equipment for electrical systems have become available in recent years, but SF₆ is still widely in use in electrical switch gear (Simmonds et al., 2020). The global lifetime of SF₆ has been revised from 3200 years to about 1000 years (Kovács et al., 2017; Ray et al., 2017) with implications for climate emissions metrics (Section 7.6.2). NF₃, which is used in the semi-conductor industry, increased 147% over the same period to 2.05 ppt in 2019. Its contribution to ERF remains small, however, at 0.0004 W m^–2. The atmospheric abundance of SO₂ f₂, which is used as a fumigant in place of ozone-depleting methyl bromide, reached 2.5 ppt in 2019, a 46% increase from 2011. Its ERF also remains small at 0.0005 W m^–2.

Perfluorocarbons CF₄ and C₂ f₆, which have exceedingly long global lifetimes, showed modest increases from 2011 to 2019. CF₄, which has both natural and anthropogenic sources, increased 8.2% to 85.5 ppt, and C₂ f₆ increased 16.3% to 4.85 ppt. c^–C₄F₈, which is used in the electronics industry and may also be generated during the production of polytetrafluoroethylene (PTFE, also known as ‘Teflon’) and other fluoropolymers (Mühle et al., 2019), has increased 34% since 2011 to 1.75 ppt, although its ERF remains below 0.001 W m^–2. Other PFCs, present at mixing ratios <1 ppt, have also been quantified (Droste et al., 2020; see Annex III).

Anthropogenic aerosol is predominantly found in the fraction of particles with radii <1 µm that comprise the fine-mode AOD (AODf; Figure 2.9d; Kinne, 2019). A significant decline in AODf of more than 1.5% per year from 2000 to 2019 has occurred over Europe and North America, while there have been positive trends of up to 1.5% per year over Southern Asia and East Africa. The global-scale trend in AODf of –0.03% per year (Figure 2.9) is significant. The results are consistent with trend estimates from an aerosol reanalysis (Bellouin et al., 2020), and the trends in satellite-derived cloud droplet number concentrations are consistent with the aerosol trends (Cherian and Quaas, 2020). Cloudiness and cloud radiative properties trends are, however, less conclusive possibly due to their large variability (Norris et al., 2016; Cherian and Quaas, 2020). Further details on aerosol-cloud interactions are assessed in Section 7.3.3.2.

The atmospheric concentrations of WMGHGs (Section 2.2.3) have continuously increased since the early 19th century, with CO₂ contributing the largest share of the positive ERF. Compared to the last two decades of the 20th century, the growth rate of CO₂ in the atmosphere increased in the 21st century, showed strong fluctuations for CH₄, and was about constant for N₂O. Mixing ratios of the most abundant CFCs declined (Section 2.2.4). Mixing ratios of HCFCs increased, but growth rates are starting to decelerate. Mixing ratios of HFCs and some other human-made components are increasing (Section 2.2.4). The ERF for CO₂ alone is stronger than for all the other anthropogenic WMGHGs taken together throughout the industrial period, and its relative importance has increased in recent years (Figures 2.10 and 7.6).

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This chapter assesses the extent to which the climate system has been affected by human influence and to what extent climate models are able to simulate observed mean climate, changes and variability. This assessment is the basis for understanding what impacts of anthropogenic climate change may already be occurring and informs our confidence in climate projections. Moreover, an understanding of the amount of human-induced global warming to date is key to assessing our status with respect to the Paris Agreement goals of holding the increase in global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C (UNFCCC, 2016).

This chapter starts with a brief description of methods for detection and attribution of observed changes in Section 3.2, which builds on the more general introduction to attribution approaches in the Cross-Working Group Box on Attribution in Chapter 1. In this chapter we assess the detection of anthropogenic influence on climate on large spatial scales and long temporal scales, a concept related to, but distinct from, that of the emergence of anthropogenically-induced climate change from the range of internal variability on local scales and shorter time scales (Section 1.4.2.2). The following sections address the climate system component by component, in each case assessing human influence and evaluating climate models’ simulations of the relevant aspects of climate and climate change. This chapter assesses the evaluation and attribution of global, hemispheric, continental and ocean basin-scale indicators of climate change in the atmosphere and at the Earth’s surface (Section 3.3, cryosphere (Section 3.4, ocean (Section 3.5, and biosphere (Section 3.6, and the evaluation and attribution of modes of variability (Section 3.7), the period of slower warming in the early 21st century (Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross-Chapter Box 3.2). Model evaluation and attribution on sub-continental scales are not covered here, since these are assessed in the Atlas and in Chapter 10, and extreme event attribution is not covered since it is assessed in Chapter 11. Section 3.8 assesses multivariate attribution and integrative measures of model performance based on multiple variables, as well as process representation in different classes of models. The chapter structure is summarized in Figure 3.1.

Considering the difficulty in accounting for climate modelling uncertainties in the regression-based approaches, Ribes et al. (2017) introduced a new statistical inference framework based on an additivity assumption and likelihood maximization, which estimates climate model uncertainty based on an ensemble of opportunity and tests whether observations are inconsistent with internal variability and consistent with the expected response from climate models. The method was further developed by Ribes et al. (2021), who applied it to narrow the uncertainty range in the estimated human-induced warming. Hannart and Naveau (2018), on the other hand, extended the application of standard causal theory (Pearl, 2009) to the context of detection and attribution by converting a time series into an event, and calculating the probability of causation, an approach which maximizes the causal evidence associated with the forcing. On the other hand, Schurer et al. (2018) employed a Bayesian framework to explicitly consider climate modelling uncertainty in the optimal regression method. Application of these approaches to attribution of large-scale temperature changes supports a dominant anthropogenic contribution to the observed global warming.

Climate change signals can vary with time and discriminant analysis has been used to obtain more accurate estimates of time-varying signals, and has been applied to different variables such as seasonal temperatures (Jia and DelSole, 2012) and the South Asian monsoon (Srivastava and DelSole, 2014). The same approach was applied to separate aerosol forcing responses from other forcings (X. Yan et al., 2016) and results using climate model output indicated that detectability of the aerosol response is maximized by using a combination of temperature and precipitation data. Paeth et al. (2017) introduced a detection and attribution method applicable for multiple variables based on a discriminant analysis and a Bayesian classification method. Finally, a systematic approach has been proposed to translating quantitative analysis into a description of confidence in the detection and attribution of a climate response to anthropogenic drivers (Stone and Hansen, 2016).

Surface temperature change is the aspect of climate in which the climate research community has had most confidence over past IPCC assessment reports. This confidence comes from the availability of longer observational records compared to other indicators, a large response to anthropogenic forcing compared to variability in the global mean, and a strong theoretical understanding of the key thermodynamics driving its changes (Collins et al., 2010; Shepherd, 2014). The AR5 assessed that it was extremely likely that human activities had caused more than half of the observed increase in global mean surface temperature from 1951 to 2010, and virtually certain that internal variability alone could not account for the observed global warming since 1951 (Bindoff et al., 2013). The AR5 also assessed with very high confidence that climate models reproduce the general features of the global-scale annual mean surface temperature increase over 1850–2011 and with high confidence that models reproduce global and Northern Hemisphere temperature variability on a wide range of time scales (Flato et al., 2013). This section assesses the performance of the new generation CMIP6 models (see Table AII.5) in simulating the patterns, trends, and variability of surface temperature, and the evidence from detection and attribution studies of human influence on large-scale changes in surface temperature.

In addition, new literature suggests that anthropogenic forcing itself may locally increase or decrease variability in surface temperatures (Screen et al., 2014; Qian and Zhang, 2015; Brown et al., 2017; Park et al., 2018; Santer et al., 2018; Weller et al., 2020). These studies imply limitations in the use of pre-industrial control simulations to quantify the role of unforced variability over the historical period. Some recent attribution studies (Gillett et al., 2021; Ribes et al., 2021) have estimated variability from ensembles of forced simulations instead, which would be expected to resolve any such changes in variability.

The previous paragraph took an ensemble-mean view of model performance, but individual models disagree on unforced variability. Figure 3.6 illustrates the large differences in GSAT variability in unforced CMIP6 pre-industrial control simulations, following the method of Parsons et al. (2020). Surface temperatures in pre-industrial conditions are especially variable in the ten models highlighted in Figure 3.6a, and some models substantially exceed the variability seen in CMIP5 models (Parsons et al., 2020). Figure 3.6b shows that the distribution of warming trends simulated by CMIP6 models in historical simulations is clearly distinct from that simulated in unforced pre-industrial control simulations. Still, the unforced variability of the five most variable models approaches half that observed over the historical period under anthropogenically forced conditions (Figure 3.6c; Parsons et al., 2020; Ribes et al., 2021). For the Centre National de la Recherche Météorologique (CNRM) models, which are among the most variable, the large, low-frequency variability is attributed to strong simulated Atlantic Multi-decadal Variability (Séférian et al., 2019; Voldoire et al., 2019b), which is difficult to rule out because of the short observational record (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns of temperature variability simulated by even the most variable models differ from the pattern of forced temperature change (Parsons et al., 2020). Taken together, this discussion and Figures 3.2, 3.5 and 3.6 indicate that the statistics of internal variability in models compare well in most cases to observational estimates and temperature proxy reconstructions, though some CMIP6 models appear to have higher multi-decadal variability than CMIP5 models or proxy reconstructions. When used in attribution studies, models with overestimated variability would increase estimated uncertainties and make results statistically conservative.

Looking at periods preceding the instrumental record, AR5 assessed with high confidence that the 20th century annual mean surface temperature warming reversed a 5000-year cooling trend in Northern Hemisphere mid- to high latitudes caused by orbital forcing, and attributed the reversal to anthropogenic forcing with high confidence (see also (Section 2.3.1.1). Since AR5, the combined response to solar, volcanic and greenhouse gas forcing was detected in all Northern Hemisphere continents (PAGES 2k-PMIP3 group, 2015) over the period 864 to 1840. In contrast, the effect of those forcings was not detectable in the Southern Hemisphere (Neukom et al., 2018). Global and Northern Hemisphere temperature changes from reconstructions over this period have been attributed mostly to volcanic forcing (Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016; PAGES 2k Consortium, 2019; Büntgen et al., 2020), with a smaller role for changes in greenhouse gas forcing, and solar forcing playing a minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019).

In contrast, studies published since AR5 indicate that closely constraining the separate contributions of greenhouse gas changes and aerosol changes to observed temperature changes remains challenging. Nonetheless, attribution of warming to greenhouse gas forcing has been found as early as the end of the 19th century (Schurer et al., 2014; Owens et al., 2017; PAGES 2k Consortium, 2019). Hegerl et al. (2019) found that volcanism cooled global temperatures by about 0.1°C between 1870 and 1910, then a lack of volcanic activity warmed temperatures by about 0.1°C between 1910 and 1950, with anthropogenic aerosols cooling temperatures throughout the 20th century, especially between 1950 and 1980 when the estimated range of aerosol cooling was about 0.1°C to 0.5°C. Jones et al. (2016) attributed a warming of 0.87 to 1.22°C per century over the period 1906 to 2005 to greenhouse gases, partially offset by a cooling of −0.54°C to −0.22°C per century attributed to aerosols. But they also found that detection of the greenhouse gas or the aerosol signal often fails, because of uncertainties in modelled patterns of change and internal variability. That point is illustrated by Figure 3.7, which shows two- and three-way fingerprinting regression coefficients for 13 CMIP6 models and the corresponding attributable warming ranges, derived using HadCRUT4 (Gillett et al., 2021). Regression coefficients with an uncertainty range that includes zero mean that detection has failed. Models with regression coefficients significantly less than one significantly overpredict the temperature response to the corresponding forcing. Conversely, models with regression coefficients significantly greater than one underpredict the response to these forcings. While estimates of warming attributable to anthropogenic influence derived using individual models are generally consistent, estimates of warming attributable to greenhouse gases and aerosols separately based on individual models are not all consistent, and detection of the aerosol influence fails more often than that of greenhouse gases. Hence, results of recent studies emphasize the need to use multi-model means to better constrain estimates of GSAT changes attributable to greenhouse gas and aerosol forcing (Schurer et al., 2018; Gillett et al., 2021; Ribes et al., 2021).

Figure 3.8 compares attributable changes in globally complete GSAT for the period 2010–2019 relative to 1850–1900 from three detection and attribution studies, two of which use CMIP6 multi-model means (Gillett et al., 2021; Ribes et al., 2021), and an estimate based on assessed effective radiative forcing and transient and equilibrium climate sensitivity (see Section 7.3.5.3). The reference period 1850–1900 is used to assess attributable temperature changes because this is when the earliest gridded surface temperature records start, this is when the CMIP6 historical simulations start, this is the earliest base period used in attribution literature, and this is a reference period used in IPCC SR1.5 and earlier reports. It should, however, be noted that Cross-Chapter Box 1.2 assesses with medium confidence that there was an anthropogenic warming with a likely range of 0.0°C–0.2°C between 1750 and 1850–1900. Figure 3.8 also shows the GSAT changes directly simulated in response to these forcings in thirteen CMIP6 models. In spite of their different methodologies and input datasets, the three attribution approaches yield very similar results, with the anthropogenic attributable warming range encompassing observed warming, and the natural attributable warming being close to zero. The warming driven by greenhouse gas increases is offset in part by cooling due to other anthropogenic forcing agents, mostly aerosols, although uncertainties in these contributions are larger than the uncertainty in the net anthropogenic warming, as discussed above. Estimates based on physical understanding of forcing and ECS made by (Chapter 7 are close to estimates from attribution studies, despite being the products of a different approach. This agreement enhances confidence in the magnitude and causes of attributable surface temperature warming.

The AR5 found high confidence for a major role for anthropogenic forcing in driving warming over each of the inhabited continents, except for Africa where they found only medium confidence because of limited data availability (Bindoff et al., 2013). At the hemispheric scale, Friedman et al. (2020) and Bonfils et al. (2020) detected an anthropogenically forced response of inter-hemispheric contrast in surface temperature change, which has a complex time evolution but shows the Northern Hemisphere cooling relative to the Southern Hemisphere until around 1975 but then warming after that. Bonfils et al. (2020) attribute the Northern Hemisphere reversal to a combination of reduced aerosol forcing and greenhouse gas induced warming of Northern Hemisphere land masses. Friedman et al. (2020) found that CMIP5 models simulate the correct sign of the inter-hemispheric contrast when forced with all forcings but underestimate its magnitude. Figure 3.9 shows global surface temperature change in CMIP6 all-forcing and natural-only simulations globally, averaged over continents, and separately over land and ocean surfaces. All-forcing simulations encompass observed temperature changes for all regions, while natural-only simulations fail to do so in recent decades except in Antarctica, based on the annual means shown. As stated above, warming results from a partial offset of greenhouse gas warming by aerosol cooling. That offset is stronger over land than ocean. Regionally, models show a large range of possible temperature responses to greenhouse gas and aerosol forcing, which complicates single-forcing attribution. A more detailed discussion of regional attribution can be found in Section 10.4. Over global land surfaces, Chan and Wu (2015) used CMIP5 simulations to attribute a warming trend of 0.3 (2.5%–97.5% confidence interval: 0.2–0.36) °C per decade to anthropogenic forcing, with natural forcing only contributing 0.05 (0.02–0.06) °C per decade. Accounting for unsampled sources of uncertainty and the availability of only a single study, their result suggests that it is very likely that human influence is the main driver of warming over land.

In summary, since the publication of AR5, new literature has emerged that better accounts for methodological and climate model uncertainties in attribution studies (Ribes et al., 2017; Hannart and Naveau, 2018) and that concludes that anthropogenic warming is approximately equal to observed warming over the 1951–2010 period. The IPCC SR1.5 reached the same conclusion for 2017 relative to 1850–1900 based on anthropogenic warming and associated uncertainties calculated using the method of Haustein et al. (2017). Moreover, the improved understanding of the causes of the apparent slowdown in warming over the beginning of the 21st century and the difference in simulated and observed warming trends over this period (Cross-Chapter Box 3.1) further improve our confidence in the assessment of the dominant anthropogenic contribution to observed warming. In deriving our assessments, these considerations are balanced against new literature that raises questions about the ability of some models to simulate variability in surface temperatures over a range of time scales (Laepple and Huybers, 2014; Parsons et al., 2017; Friedman et al., 2020), and the finding that some CMIP6 models exhibit substantially higher multi-decadal internal variability than that seen in CMIP5, which remains to be fully understood (Parsons et al., 2020; Ribes et al., 2021). Further, uncertainties in simulated aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting in very diverse spatial responses of different climate models to aerosol forcing, and inter-model differences in the historical global mean temperature evolution and in diagnosed cooling attributable to aerosols (Figure 3.8). Moreover, like previous generations of coupled model simulations, historical and single forcing CMIP6 simulations follow a common experimental design (Eyring et al., 2016a; Gillett et al., 2016) and are thus all driven by the same common set of forcings, even though these forcings are uncertain. Hence, forcing uncertainty is not directly accounted for in most of the attribution and model evaluation studies assessed here, although this limitation can to some extent be addressed by comparing with previous generation multi-model ensembles or individual model studies using different sets of forcings.

The IPCC SR1.5 best estimate and likely range of anthropogenic attributable GMST warming was 1.0 ± 0.2°C in 2017 with respect to the period 1850–1900. Here, the best estimate is expressed in terms of GSAT and is calculated as the average of the three estimates shown in Figure 3.9, yielding a value of 1.07°C. Ranges for attributable GSAT warming are derived by finding the smallest ranges with a precision of 0.1°C which span all of the 5–95% ranges from the attribution studies shown in Figure 3.9. These ranges are then assessed as likely rather than very likely because the studies may underestimate the importance of the structural limitations of climate models, which probably do not represent all possible sources of internal variability; use too simple climate models, which may underestimate the role of internal variability; or underestimate model uncertainty, especially when using model ensembles of limited size and inter-dependent models, for example through common errors in forcings across models, as discussed above. This leads to a likely range for anthropogenic attributable warming in 2010–2019 relative to 1850–1900 of 0.8 to 1.3°C in terms of GSAT. This range encompasses the best estimate and very likely range of observed GSAT warming of 1.06 [0.88 to 1.21] °C over the same period (Cross-Chapter Box 2.3). There is medium confidence that the best estimate and likely ranges of attributable warming expressed in terms of GMST are equal to those for GSAT (Cross-Chapter Box 2.3). Repeating the process for other time periods leads to the best estimates and likely ranges listed in Table 3.1. GSAT change attributable to natural forcings is −0.1 to +0.1°C. The likely range of GSAT warming attributable to greenhouse gases is assessed in the same way to be 1.0 to 2.0°C while the GSAT change attributable to aerosols, ozone and land-use change is −0.8 to 0.0°C. Progress in attribution techniques allows the important advance of attributing observed surface temperature warming since 1850–1900, instead of since 1951 as was done in AR5.

The IPCC SR1.5 gave a likely range for the human-induced warming rate of 0.1°C to 0.3°C per decade in 2017, with a best estimate of 0.2°C per decade (Allen et al., 2018). Table 3.1 lists the estimates of attributable anthropogenic warming rate over the period 2010–2019 based on the three studies that underpin the assessment of GSAT warming (Haustein et al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from Haustein et al. (2017), based on observed warming, and Ribes et al. (2021), based on CMIP6 simulations constrained by observed warming, are in good agreement. The Gillett et al. (2021) estimate, also based on CMIP6 models, corresponds to a larger anthropogenic attributable warming rate, because of a smaller warming rate attributed to natural forcing than in Ribes et al. (2021). This disagreement does not support a decrease in uncertainty compared to the SR1.5 assessment. So the range for anthropogenic attributable surface temperature warming rate of 0.1°C to 0.3°C per decade is again assessed to be likely, with a best estimate of 0.2°C per decade.

(Chapter 2 assessed that the troposphere has warmed since at least the 1950s, that it is virtually certain that the stratosphere has cooled, and that there is medium confidence that the upper troposphere in the tropics has warmed faster than the near-surface since at least 2001 (Section 2.3.1.2). The AR5 assessed that anthropogenic forcings, dominated by greenhouse gases, likely contributed to the warming of the troposphere since 1961 and that anthropogenic forcings, dominated by the depletion of the ozone layer due to ozone-depleting substances, very likely contributed to the cooling of the lower stratosphere since 1979. Since AR5, understanding of observational uncertainties in the radiosonde and satellite data has improved with more available data and longer coverage, and differences between models and observations in the tropical atmosphere have been investigated further.

Several studies since AR5 have continued to demonstrate an inconsistency between simulated and observed temperature trends in the tropical troposphere, with models simulating more warming than observations (Mitchell et al., 2013, 2020; Santer et al., 2017a, b; McKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al. (2017b) used updated and improved satellite retrievals to investigate model performance in simulating the tropical mid- to upper-troposphere trends, and removed the influence of stratospheric cooling by regression. These factors were found to reduce the size of the discrepancy in mid- to upper-tropospheric temperature trends between models and observations over the satellite era, but a discrepancy remained. Santer et al. (2017a) found that during the late 20th century, the discrepancies between simulated and satellite-derived mid- to upper-tropospheric temperature trends were consistent with internal variability, while during most of the early 21st century, simulated tropospheric warming was significantly larger than observed, which they related to systematic deficiencies in some of the external forcings used after year 2000 in the CMIP5 models. However, in CMIP6, differences between simulated and observed upper-tropospheric temperature trends persist despite updated forcing estimates (Mitchell et al., 2020). Figure 3.10 shows that CMIP6 models forced by combined anthropogenic and natural forcings overestimate temperature trends compared to radiosonde data (Haimberger et al., 2012) throughout the tropical troposphere (Mitchell et al., 2020). Over the 1979–2014 period, models are more consistent with observations in the lower troposphere, and least consistent in the upper troposphere around 200 hPa, where biases exceed 0.1°C per decade. Several studies using CMIP6 models suggest that differences in climate sensitivity may be an important factor contributing to the discrepancy between the simulated and observed tropospheric temperature trends (McKitrick and Christy, 2020; Po-Chedley et al., 2021), though it is difficult to deconvolve the influence of climate sensitivity, changes in aerosol forcing and internal variability in contributing to tropospheric warming biases (Po-Chedley et al., 2021). Another study found that the absence of a hypothesized negative tropical cloud feedback could explain half of the upper troposphere warming bias in one model (Mauritsen and Stevens, 2015).

The AR5 assessed as likely that anthropogenic forcings, dominated by greenhouse gases, contributed to the warming of the troposphere since 1961 (Bindoff et al., 2013). Since then, there has been further progress in detecting and attributing tropospheric temperature changes. Mitchell et al. (2020) used CMIP6 models to find that the main driver of tropospheric temperature changes is greenhouse gases. Previous detection of the anthropogenic influence on tropospheric warming may have overestimated uncertainties: Pallotta and Santer (2020) found that CMIP5 climate models overestimate the observed natural variability in global mean tropospheric temperature on time scales of 5–20 years. Nevertheless, Santer et al. (2019) found that stochastic uncertainty is greater for tropospheric warming than stratospheric cooling because of larger noise and slower recovery time from the Mount Pinatubo eruption in the troposphere. The detection time of the anthropogenic signal in the tropospheric warming can be affected by both the model climate sensitivity and the model response to aerosol forcing. Volcanic forcing is also important, as models that do not consider the influence of volcanic eruptions in the early 21st century overestimate the observed tropospheric warming since 1998 (Santer et al., 2014). Changes in the amplitude of the seasonal cycle of tropospheric temperatures have also been attributed to human influence. Santer et al. (2018) found that satellite data and climate models driven by anthropogenic forcing show consistent amplitude increases at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics.

In summary, these studies confirm the dominant role of human activities in tropospheric temperature trends. We therefore assess that it is very likely that anthropogenic forcing, dominated by greenhouse gases, was the main driver of the warming of the troposphere since 1979.

The AR5 concluded that the CMIP5 models simulated a generally realistic evolution of lower-stratospheric temperatures (Bindoff et al., 2013; Flato et al., 2013), which was better than that of the CMIP3 models, in part because they generally include time-varying ozone concentrations, unlike many of the CMIP3 models. Nonetheless, it was noted that there was a tendency for the simulations to underestimate stratospheric cooling compared to observations. Bindoff et al. (2013) concluded that it was very likely that anthropogenic forcing, dominated by stratospheric ozone depletion by chemical reactions involving trace species known as ozone-depleting substances (ODS), had contributed to the cooling of the lower stratosphere since 1979. Increased greenhouse gases cause near-surface warming but cooling of stratospheric temperatures.

Upper stratospheric temperature changes were not assessed in the context of attribution or model evaluation in AR5, but this is an area where there has been considerable progress over recent years (Section 2.3.1.2.1). Simulated temperature changes in chemistry-climate models show good consistency with the reprocessed dataset from NOAA STAR but are less consistent with the revised UK Met Office record (Karpechko et al., 2018). The latter still shows stronger cooling than simulated in chemistry-climate models (Maycock et al., 2018). Reanalyses, which assimilate AMSU and SSU datasets, indicate an upper-stratospheric cooling from 1979 to 2009 of about 3°C at 5 hPa and 4°C at 1 hPa that agrees well with the cooling in simulations with prescribed SST and using CMIP5 forcings (Simmons et al., 2014). Mitchell (2016) used regularized optimal fingerprinting techniques to carry out an attribution analysis of annual mid- to upper-stratospheric temperature in response to external forcings. They found that anthropogenic forcing has caused a cooling of approximately 2°C–3°C in the upper stratosphere over the period of 1979–2015, with greenhouse gases contributing two thirds of this change and ozone depletion contributing one third. They found a large upper-stratospheric temperature change in response to volcanic forcing (0.4°C–0.6°C for Mount Pinatubo) but that change is still smaller than the lower-stratospheric signal. Aquila et al. (2016) found that the cooling of the middle and upper stratosphere after 1979 is mainly due to changes in greenhouse gas concentrations. Volcanic eruptions and the solar cycle were found not to affect long-term stratospheric temperature trends but to have short-term influences.

In summary, based on the latest updates to satellite observations of stratospheric temperature, we assess that simulated and observed trends in global mean temperature through the depth of the stratosphere are more consistent than based on previous datasets, but some differences remain (medium confidence). Studies published since AR5 increase our confidence in the simulated stratospheric temperature response to greenhouse gas and ozone changes, and support an assessment that it is extremely likely that stratospheric ozone depletion due to ozone-depleting substances was the main driver of the cooling of the lower stratosphere between 1979 and the mid-1990s, as expected from physical understanding. Similarly, revised observations and new studies support an assessment that it is extremely likely that anthropogenic forcing, both from increases in greenhouse gas concentrations and depletion of stratospheric ozone due to ozone-depleting substances, was the main driver of upper-stratospheric cooling since 1979.

Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015; Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Niño-like) to negative (La Niña-like) phases during the slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., 2014; Middlemas and Clement, 2016), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT relationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations over the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is constrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). Part of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent, and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable that there is large uncertainty in the magnitude of the PDV influence on GSAT across models (Deser et al., 2017a; C.-Y. Wang et al., 2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean–Warm Land (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019).

CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no net effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018), natural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield a negative contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed and CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical models (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al., 2014). However, in a single climate model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and anthropogenic aerosols) available when the study was done made no significant difference to the 1998–2012 GMST trend from that obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role (Outten et al., 2015). In the multi-model ensemble mean, the 1998–2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 than CMIP5 (Section 7.5.6). To summarize, while there is medium confidence that natural forcing that was missing in CMIP5 contributed to the difference of observed and simulated GMST trends, confidence remains low in the quantitative contribution of net forcing updates.

With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper understanding of mechanisms, there is very high confidence that the slower GMST and GSAT increase inferred from observations in the 1998–2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the continued warming of the global ocean (very high confidence) and in the continued rise of hot extremes over land (medium confidence). Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown (Hedemann et al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors (Huber and Knutti, 2014; Schmidt et al., 2014; Medhaug et al., 2017).

A fact hindering detection and attribution studies in precipitation and other hydrological variables is the large internal variability of these fields relative to the anthropogenic signal. This low signal-to-noise ratio hinders the emergence of the anthropogenic signal from natural variability. Moreover, the sign of the change depends on location and time of the year. Paleoclimate records provide valuable context for observed trends in the 20th and 21st century and assist with the attribution of these trends to human influence (see also (Section 2.3.1.3.1). By nature, hydrological proxy data represent regional conditions, but taken together can represent large-scale patterns. As an example of how paleorecords have helped assessing the origin of changes, we consider some, mainly subtropical, regions which have experienced systematic drying in recent decades (see also Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed in Section 3.3.3.2.

Records of tree ring width have provided evidence that recent prolonged dry spells in the Levant and Chile are unprecedented in the last millennium (high confidence) (Cook et al., 2016a; Garreaud et al., 2017). East Africa has also been drying in recent decades (Rowell et al., 2015; Hoell et al., 2017), a trend that is unusual in the context of the sedimentary paleorecord spanning the last millennium (Tierney et al., 2015). This may be a signature of anthropogenic forcing but cannot yet be distinguished from natural variability (Hoell et al., 2017; Philip et al., 2018). Likewise, tree rings indicate that the 2012–2014 drought in the south-western United States was exceptionally severe in the context of natural variability over the last millennium, and may have been exacerbated by the contribution of anthropogenic temperature rise (medium confidence) (Griffin and Anchukaitis, 2014; Williams et al., 2015). Furthermore, Williams et al. (2020) used a combination of hydrological modelling and tree-ring reconstructions to show that the period from 2000 to 2018 was the driest 19-year span in south-western North America since the late 1500s. Nonetheless, tree rings also indicate the presence of prolonged megadroughts in western North America throughout the last millennium that were more severe than 20th and 21st century events (high confidence) (Cook et al., 2004, 2010, 2015). These were associated with internal variability (Coats et al., 2016; Cook et al., 2016b) and indicate that large-magnitude changes in the water cycle may occur irrespective of anthropogenic influence (see also McKitrick and Christy, 2019).

The AR5 concluded that an anthropogenic contribution to increases in specific humidity is found with medium confidence at and near the surface. A levelling off of atmospheric water vapour over land in the last two decades that needed better understanding, and remaining observational uncertainties, precluded a more confident assessment (Bindoff et al., 2013). Sections 4.5.1.3 and 8.3.1.4 show that there have been significant advances in the understanding of the processes controlling land surface humidity. In particular, there has been a focus on the role of oceanic moisture transport and land-atmosphere feedbacks in explaining the observed trends in relative humidity.

The detection and attribution of tropospheric water vapour changes can be traced back to Santer et al. (2007), who used estimates of atmospheric water vapour from the satellite-based Special Sensor Microwave Imager (SSM/I) and from CMIP3 historical climate simulations. They provided evidence of human-induced moistening of the troposphere, and found that the simulated human fingerprint pattern was detectable at the 5% level by 2002 in water vapour satellite data (from 1988 to 2006). The observed changes matched the historical simulations forced by greenhouse gas changes and other anthropogenic forcings, and not those due to natural variability alone. Then, Santer et al. (2009) repeated this study with CMIP5 models, and found that the detection and attribution conclusions were not sensitive to model quality. These results demonstrate that the human fingerprint is governed by robust and basic physical processes, such as the water vapour feedback. Finally, Chung et al. (2014) extended this line of research by focusing on the global-mean water vapour content in the upper troposphere. Using satellite-based observations and sets of CMIP5 climate simulations run under various climate-forcing options, they showed that the observed moistening trend of the upper troposphere over the 1979–2005 period could not be explained by internal variability alone, but is attributable to a combination of anthropogenic and natural forcings. This increase in water vapour is accompanied by a reduction in mid-tropospheric relative humidity and clouds in the subtropics and mid-latitude in both models and observations related to changes in the Hadley cell (Section 3.3.3.1.1; Lau and Kim, 2015).

Dunn et al. (2017) confirmed earlier findings that global mean surface relative humidity increased between 1973 and 2000, followed by a steep decline (also reported in Willett et al., 2014) until 2013, and specific humidity correspondingly increased and then remained approximately constant (see also (Section 2.3.1.3.2), with none of the CMIP5 models capturing this behaviour. They noted biases in the mean state of the CMIP5 models’ surface relative humidity (and ascribed the failure to the representation of land surface processes and their response to CO₂ forcing), concluding that these biases preclude any detection and attribution assessment. On the other hand, Byrne and O’Gorman (2018) showed that the positive trend in specific humidity continued in recent years and can be detected over land and ocean from 1979 to 2016. Moreover, they provided a theory suggesting that the increase in annual surface temperature and specific humidity as well as the decrease in relative humidity observed over land are linked to warming over the neighbouring ocean. They also pointed out that the negative trend in relative humidity over land regions is quite uncertain and requires further investigation. A recent study has also identified an anthropogenically-driven decrease in relative humidity over the Northern Hemisphere mid-latitude continents in summer between 1979 and 2014, which was underestimated by CMIP5 models (Douville and Plazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020) showed that this decrease in boreal summer relative humidity over mid-latitudes is related not only to global ocean warming, but also to the physiological effect of CO₂ on plants in the land surface model.

Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the large scale patterns of annual mean land precipitation and seasonality well, as well as reproducing qualitatively the observed zonal mean land precipitation trends for the period 1948–2005: models capture the drying trends in the tropics and at 45°S and the wetting trend in the Northern Hemisphere mid- to high latitudes, but the amplitudes of the changes are much smaller than observed. Land precipitation was found to show enhanced seasonality in observations (Chou et al., 2013), qualitatively consistent with the simulated response to anthropogenic forcing (Dwyer et al., 2014). However, models do not appear to reproduce the zonal mean trends in the magnitude of the seasonal cycle over the period 1948–2005, nor the two-dimensional distributions of trends of annual precipitation and seasonality over land, but differences may be explainable by internal variability (X. Li et al., 2016b). However, observed trends in seasonality depend on data set used (X. Li et al., 2016b; Marvel et al., 2017), and Marvel et al. (2017) found that observed changes in the annual cycle phase are consistent with model estimates of forced changes. These phase changes are mainly characterized by earlier onset of the wet season on the equatorward flanks of the extratropical storm tracks, particularly in the Southern Hemisphere. Box 8.2 assesses regional changes in water cycle seasonality.

Based on long-term island precipitation records, Polson et al. (2016) identified significant increases in precipitation in the tropics and decreases in the subtropics, which are consistent with those simulated by the CMIP5 models. Moreover, results from Polson and Hegerl (2017) give support to an intensification of the water cycle according to the wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as well. Other studies suggest that this paradigm does not necessarily hold over dry regions where moisture is limited (see also Section 8.2.2.1; Greve et al., 2014; Kumar et al., 2015). Polson and Hegerl (2017) explained this discrepancy by taking into account the seasonal and interannual movement of the regions (Allan, 2014). A follow-up study using CMIP6 models also found that the observed strengthening contrast of precipitation over wet and dry regions was detectable, although the increase was significantly larger in observations than in the multi-model mean. The change was attributed to a combination of anthropogenic and natural forcings, with anthropogenic forcings detectable in multi-signal analyses (Figure 3.14; Schurer et al., 2020).

Global land precipitation has likely increased since the middle of the 20th century (medium confidence), while there is low confidence in trends in land data prior to 1950 and over the ocean during the satellite era due to disagreement between datasets (Section 2.3.1.3.4). Figure 3.15a shows the time evolution of the global mean land precipitation since 1950, as well as the trend during the period. Adler et al. (2017) found no significant trend in the global mean precipitation during the satellite era, consistent with model simulations (Wu et al., 2013) and physical understanding of the energy budget (Section 8.2.1). This has been suggested to be due to the negative effect of anthropogenic sulphate aerosol that opposed the positive influence of rising global mean temperatures due to greenhouse gases (Salzmann, 2016; Richardson et al., 2018). The precipitation change expected from ocean warming is also partly offset by the fast atmospheric adjustment to increasing greenhouse gases (Section 8.2.1). Over the ocean, the negligible trend may be due to the cancelling effects of CO₂ and aerosols (Richardson et al., 2018).

A gridpoint based analysis of annual precipitation trends over land regions since 1901 (Knutson and Zeng, 2018) comparing observed and simulated trends found that detectable anthropogenic increasing trends have occurred prominently over many mid- to high-latitude regions of the Northern Hemisphere and subtropics of the Southern Hemisphere. The observed trends in many cases are significantly stronger than modelled in the CMIP5 historical runs for the 1901–2010 period (though not for 1951–2010), which may be due to disagreement between observed datasets (Section 2.3.1.3.4), and/or suggest possible deficiencies in models.

The observed precipitation increase in the Northern Hemisphere high latitudes over the period 1966–2005 was attributed to anthropogenic forcing by a study using CMIP5 models (Wan et al., 2015) supporting the AR5 assessment. Initial results from CMIP6 also support the role of anthropogenic forcing in the precipitation increase observed in Northern Hemisphere high latitudes (see Figure 3.15c): the observed positive trend detected for the band 60°N–90°N can only be reproduced when anthropogenic forcing is included, although models tend to simulate overall a larger positive trend. A similar positive trend, but less significant, is also detected between 30°N–60°N, while in the southern mid-latitudes no trend is simulated (see Figure 3.15d, f).

Overall, studies published since AR5 provide further evidence of an anthropogenic influence on precipitation, and therefore we now assess that it is likely that human influence has contributed to large-scale precipitation changes observed since the mid-20th century. New attribution studies strengthen previous findings of a detectable increase in mid to high latitude land precipitation over the Northern Hemisphere (high confidence). There is medium confidence that human influence has contributed to a strengthening of the zonal mean wet tropics-dry subtropics contrast, and that tropical rainfall changes follow the wet-gets-wetter, dry-gets-drier paradigm. There is also medium confidence that ozone depletion has increased precipitation over the southern high latitudes and decreased it over southern mid-latitudes during austral summer. Owing to observational uncertainties and inconsistent results between studies, we conclude that there is low confidence in the attribution of changes in the seasonality of precipitation.

Streamflow is to-date the only variable of the terrestrial water cycle with enough in-situ observations to allow for detection and attribution analysis at continental to global scales. Based on evidence from a few formal detection and attribution studies, particularly on the timing of peak streamflow, and the qualitative evaluation of studies reporting on observed and simulated trends, AR5 concluded that there is medium confidence that anthropogenic influence on climate has affected streamflow in some middle and high latitude regions (Bindoff et al., 2013). The AR5 also noted that observational uncertainties are large and that often only a limited number of models were considered.

(Section 2.3.1.3.6 assesses that there have not been significant trends in global average streamflow over the last century, though regional trends have been observed, driven in part by internal variability. Only a limited number of studies have systematically compared observed streamflow trends at continental to global scales with changes simulated by global circulation models in a detection and attribution setting. H. Yang et al. (2017) did not find a significant correlation between observed runoff changes and changes simulated in CMIP5 models in most grid cells, consistent with the assessment that observed changes are dominated by internal variability. In a pan-European assessment, Gudmundsson et al. (2017) attributed the spatio-temporal pattern of decreasing streamflow in southern Europe and increasing streamflow in northern Europe to anthropogenic climate change, but also concluded that additional effects of human water withdrawals could not be excluded. Focussing on continental runoff between 1958 and 2004, Alkama et al. (2013) found a significant change only when using reconstructed data over all rivers, and a large uncertainty in the estimate of the global streamflow trend due to opposing changes over different continents. Gedney et al. (2014) detected the influence of aerosols on streamflow in North America and Europe, with aerosols having driven an increase in streamflow due to reduced evaporation (see Section 8.3.1.5 for details on processes). There is also evidence for a detectable anthropogenic contribution toward earlier winter-spring streamflows in the north-central US (Kam et al., 2018) and in western Canada (Najafi et al., 2017). From a model evaluation perspective, Sheffield et al. (2013) reported that CMIP5 models reproduce spatial variations in runoff in North America well, though they tend to underestimate it.

In summary, there is medium confidence that anthropogenic climate change has altered local and regional streamflow in various parts of the world and that the associated global-scale trend pattern is inconsistent with internal variability. Moreover, human interventions and water withdrawals, while affecting streamflow, cannot explain the observed spatio-temporal trends (medium confidence).

One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature (Seneviratne et al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence (Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

(Chapter 11 assesses that there is high confidence that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean temperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes globally and over most continents over the 1951–2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol influences are less clearly separable in observed changes in cold extremes.

An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models (high confidence), and there is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is high confidence that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings (Dong et al., 2020; Paik et al., 2020b).

Observational SST products indicate that the equatorial zonal SST gradient from the western to the eastern equatorial Pacific has strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical simulations on average simulate a weakening, large ensemble simulations span the observed strengthening since the 1950s (Watanabe et al., 2021) suggesting an important contribution from internal variability. Coats and Karnauskas (2017) also find that the anthropogenic influence on the SST gradient is yet to emerge out of internal variability even on centennial time scales.

Trends since the 1980s in in-situ and satellite observations and reanalyses exhibit strengthening of the Pacific Walker circulation and SST gradient (Section 2.3.1.4.1 and Figure 3.16f; L’Heureux et al., 2013; Boisséson et al., 2014; England et al., 2014; Kociuba and Power, 2015; Ma and Zhou, 2016). AMIP simulations reproduce this strengthening (Figure 3.16d; Boisséson et al., 2014; Ma and Zhou, 2016), indicating a dominant role of SST changes. However, all reanalysis trends lie outside the 5–95% range of simulated CMIP6 historical Walker circulation trends over this period (Figure 3.16f), consistent with CMIP5 results (England et al., 2014; Kociuba and Power, 2015). This may be in part caused by the underestimation of the PDV magnitude especially in CMIP5 models (Section 3.7.6; Kociuba and Power, 2015; Chung et al., 2019), but also suggests a potential error in simulating the forced changes of the Walker circulation. Specifically, anthropogenic and volcanic aerosol changes over this period may have driven a strengthening (DiNezio et al., 2013; Takahashi and Watanabe, 2016; Hua et al., 2018). This aerosol influence may be indirect via Atlantic Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin teleconnections (McGregor et al., 2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), which may be underestimated in models due to SST biases in the equatorial Atlantic (Section 3.5.1.2.2; McGregor et al., 2018). Note also the large uncertainty in aerosol influence on the Walker circulation (Kuntz and Schrag, 2016; Hua et al., 2018; Oudar et al., 2018), which is also seen in CMIP6 (Figure 3.16f).

In instrumental records, global summer monsoon precipitation intensity (measured by summer precipitation averaged over the monsoon domain) decreased from the 1950s to 1980s, followed by an increase (Section 2.3.1.4.2 and Figure 3.17c), arising mainly from variations in Northern Hemispheric land monsoons. A CMIP5 multi-model study by Y. Zhang et al. (2018) found that observed 1951–2004 trends of the global and Northern Hemisphere summer land monsoon precipitation intensity are well captured by historical simulations, and CMIP6 models show similar results for global land summer monsoon precipitation (Figure 3.17c). However, the 1960s peak in the Northern Hemisphere summer monsoon circulation is outside the 5th–95th percentile range of CMIP5 and CMIP6 historical simulations for two out of three reanalyses (Figure 3.17d). Modelling studies show that greenhouse gas increases act to enhance Northern Hemisphere summer monsoon precipitation intensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L. Zhang et al., 2018b). Since the mid-20th century, however, modelling studies show that this effect was overwhelmed by the influence of anthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al., 2015; Y. Zhang et al., 2018; Giannini and Kaplan, 2019) and in CMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation and reduction of moisture availability are important in this aerosol influence (T. Zhou et al., 2020). Besides these human influences, the global monsoon is sensitive to internal variability and natural forcing including ENSO and volcanic aerosols on interannual time scales and PDV and AMV on decadal to multi-decadal time scales (Wang et al., 2013, 2018; F. Liu et al., 2016; Jiang and Zhou, 2019; Zuo et al., 2019); though AMV in the 20th century may have been partly driven by aerosols, see Section 3.7.7. Indeed, AMIP simulations better reproduce the observed multi-decadal variations of the global monsoon precipitation and circulation (Figure 3.17c,d). Y. Zhang et al. (2018) find that the multi-model ensemble mean trend of global land monsoon precipitation in historical simulations, dominated by anthropogenic aerosol forcing contributions, emerges out of the 90% range of internally-driven trends in pre-industrial control simulations. However, it should be noted that CMIP5 models tend to under-represent the PDV magnitude (Section 3.7.6), suggesting potential overconfidence in the detection of the forced signal. An observed enhancement in global summer monsoon precipitation since the 1980s is accompanied by an intensification of the Northern Hemisphere summer monsoon circulation (Figure 3.17c,d). These trends appear to be at the extreme of the range of the CMIP6 historical simulation ensemble but are well captured by AMIP simulations (Figure 3.17c,d). While the precipitation increase is consistent with greenhouse gas forcing, the circulation intensification is opposite to the simulated response to greenhouse gas forcing, and these enhancements have been attributed to PDV and AMV (Wang et al., 2013; Kamae et al., 2017).

In summary, while greenhouse gas increases acted to enhance the global land monsoon precipitation over the 20th century (medium confidence), consistent with projected future enhancement (Section 4.5.1.5), this tendency was overwhelmed by anthropogenic aerosols from the 1950s to the 1980s, which contributed to weakening of global land summer monsoon precipitation intensity for this period (medium confidence). There is medium confidence that the intensification of global monsoon precipitation and Northern Hemisphere summer monsoon circulation since the 1980s is dominated by internal variability. These assessments are supported respectively by multi-model detection and attribution studies which find an important role for anthropogenic aerosols in the weakening trend, and studies that identify a role for AMV and PDV in inducing the Northern Hemisphere summer monsoon circulation enhancement since the 1980s. Supported by multi-model simulations that are qualitatively consistent with proxy evidence, there is high confidence that orbital forcing contributed to higher Northern Hemisphere monsoon precipitation in the mid-Pliocene and mid-Holocene than pre-industrial. While CMIP5 models can capture the domain and precipitation intensity of the global monsoon, biases remain in their regional representations, and they are unsuccessful in quantitatively reproducing changes in paleo reconstructions (high confidence). CMIP6 models reproduce the domain and precipitation intensity of the global monsoon observed over the instrumental period better than CMIP5 models (medium confidence). However, CMIP5 and CMIP6 models fail to fully capture the variations of the Northern Hemisphere summer monsoon circulation (Figure 3.17d), but there is low confidence in this assessment due to a lack of evidence in the literature.

In summary, there is low confidence that an observed decrease in the frequency of Northern Hemisphere summertime extratropical cyclones is linked to anthropogenic influence. In the Southern Hemisphere, there is high confidence that human influence, in the form of ozone depletion, has contributed to the observed poleward shift of the jet in austral summer, while confidence is low for human influence on historical blocking activity. The low confidence statements are due to the limited number of studies available. The shift of the Southern Hemisphere jet is correlated with modulations of the SAM (Section 3.7.2). There is medium confidence in model performance regarding the simulation of the extratropical jets, storm track and blocking activity, with increased resolution sometimes corresponding to better performance, but important shortcomings remain, particularly for the Euro-Atlantic sector of the Northern Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–3.3.3.3, there is high confidence that CMIP6 models capture the general characteristics of the tropospheric large-scale circulation.

In summary, an anthropogenic influence on the frequency or other aspects of SSWs has not yet been robustly detected. There is low confidence in the ability of models to simulate any such trends over the historical period because of large natural interannual variability and also due to substantial common biases in the simulated mean state affecting the simulated frequency of SSWs.

The AR5 concluded that ‘anthropogenic forcings are very likely to have contributed to Arctic sea ice loss since 1979’ (Bindoff et al., 2013), based on studies showing that models can reproduce the observed decline only when including anthropogenic forcings, and formal attribution studies. Since the beginning of the modern satellite era in 1979, Northern Hemisphere sea ice extent has exhibited significant declines in all months with the largest reduction in September (see Section 2.3.2.1.1, and Figures 3.20 and 3.21 for more details on observed changes). The recent Arctic sea ice loss during summer is unprecedented since 1850 (high confidence), but as in AR5 and SROCC there remains only medium confidence that the recent reduction is unique during at least the past 1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). CMIP5 models also simulate Northern Hemisphere sea ice loss over the satellite era but with large differences among models (e.g., Massonnet et al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss across model simulations encompasses the observed change, although observations fall near the low end of the CMIP5 and CMIP6 distributions of trends (Figure 3.20). CMIP6 models on average better capture the observed Arctic sea ice decline, albeit with large inter-model spread. Notz et al. (2020) found that CMIP6 models better reproduce the sensitivity of Arctic sea ice area to CO₂ emissions and global warming than earlier CMIP models although the models’ underestimation of this sensitivity remains. Davy and Outten (2020) also found that CMIP6 models can simulate the seasonal cycle of Arctic sea ice extent and volume better than CMIP5 models. For the assessment of physical processes associated with changes in Arctic sea ice, see Section 9.3.1.1.

Since AR5, there have been several new detection and attribution studies on Arctic sea ice. While the attribution literature has mostly used sea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz, 2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 and 3.21. Kirchmeier-Young et al. (2017) compared the observed time series of the September SIE over the period 1979–2012 with those from different large ensemble simulations which provide a robust sampling of internal climate variability (CanESM2, CESM1, and CMIP5) using an optimal fingerprinting technique. They detected anthropogenic signals which were separable from the response to natural forcing due to solar irradiance variations and volcanic aerosol, supporting previous findings (Figure 3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke, 2012; Notz and Stroeve, 2016). Using selected CMIP5 models and three independently derived sets of observations, Mueller et al. (2018) detected fingerprints from greenhouse gases, natural, and other anthropogenic forcings simultaneously in the September Arctic SIE over the period 1953–2012. They further showed that about a quarter of the greenhouse gas induced decrease in SIE has been offset by an increase due to other anthropogenic forcing (mainly aerosols). Similarly, Gagné et al. (2017b) suggested that the observed increase in Arctic sea ice concentration over the 1950–1975 period was primarily due to the cooling contribution of anthropogenic aerosol forcing based on single model simulations. Gagné et al. (2017a) identified a detectable increase in Arctic SIE in response to volcanic eruptions using CMIP5 models and four observational datasets. Polvani et al. (2020) suggested that ozone depleting substances played a substantial role in the Arctic sea ice loss over the 1955–2005 period.

An important consideration in comparing Arctic sea ice loss in models and observations is the role of internal variability (medium confidence). Using ensemble simulations from a single model, Kay et al. (2011) suggested that internal variability could account for about half of the observed September ice loss. More recently, large ensemble simulations have been performed with many more ensemble members (Kay et al., 2015). These enable a more robust characterization of internal variability in the presence of forced anthropogenic change. Using such large ensembles, some studies discussed the influence of internal variability on Arctic sea ice trends (Swart et al., 2015). Song et al. (2016) also compared the trends in the forced and unforced simulations using multiple climate models and found that internal variability explains about 40% of the observed September sea ice melting trend, supporting previous studies (Stroeve et al., 2012). Based on the large ensembles of CESM1 and CanESM2, the September Arctic sea ice extent variance first increases and then decreases as SIE declines from its pre-industrial value (Kirchmeier-Young et al., 2017; Mueller et al., 2018) consistent with previous work (Goosse et al., 2009), but neither study found a strong sensitivity of detection and attribution results to the change in variability. Further work has indicated that internally-driven summer atmospheric circulation trends with enhanced atmospheric ridges over Greenland and the Arctic Ocean, which project on the negative phase of the North Atlantic Oscillation (Section 3.7.1), play an important role in the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al., 2017). A fingerprint analysis using the CESM large ensemble suggests that this internal variability accounts for 40–50% of the observed September Arctic sea ice decline (Ding et al., 2019; England et al., 2019). Internally-generated decadal tropical variability and associated atmospheric teleconnections were suggested to have contributed to the changing atmospheric circulation in the Arctic and the associated rapid sea ice decline from 2000 to 2014 (Meehl et al., 2018).

Based on the new attribution studies since AR5, we conclude that it is very likely that anthropogenic forcing mainly due to greenhouse gas increases was the main driver of Arctic sea ice loss since 1979. Increases in anthropogenic aerosols have offset part of the greenhouse gas induced Arctic sea ice loss since the 1950s (medium confidence). Despite large differences in the mean sea ice state in the Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6 models. Nonetheless, large inter-model differences in the Arctic sea ice decline remain, limiting our ability to quantify forced changes and internal variability contributions.

The AR5 noted the strong linear correlation between Northern Hemisphere snow cover extent (SCE) and annual-mean surface air temperature in CMIP5 models. It was assessed as likely that there had been an anthropogenic contribution to observed reductions in Northern Hemisphere snow cover since 1970 (Bindoff et al., 2013). The AR5 assessed that CMIP5 models reproduced key features of observed snow cover well, including the seasonal cycle of snow cover over northern regions of Eurasia and North America, but had more difficulties in more southern regions with intermittent snow cover. The AR5 also found that CMIP5 models underestimated the observed reduction in spring snow cover over this period (Figure 3.22; see also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria-Otín and Zolina, 2020). This behaviour has been linked to how the snow-albedo feedback is represented in models (Thackeray et al., 2018a). The CMIP5 multi-model ensemble has been shown to represent the snow-albedo feedback more realistically than CMIP3, although models from some individual modelling centres have not improved or have even got worse (Thackeray et al., 2018a). There is still a systematic overestimation of the albedo of boreal forest covered by snow (Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the snow albedo feedback might have been overestimated by CMIP5 models (Section 9.5.3; Xiao et al., 2017).

Several CMIP5 and CMIP6 based studies have consistently attributed the observed Northern Hemisphere spring SCE changes (Section 2.3.2.2) to anthropogenic influences (Rupp et al., 2013; Najafi et al., 2016; Paik and Min, 2020), with the observed changes being found to be inconsistent with natural variability alone. Similarly, spring snow thickness (Snow Water Equivalent) changes on the scale of the Northern Hemisphere have been attributed to greenhouse gas forcing (Jeong et al., 2017). Using individual forcing simulations from multiple CMIP6 models, Paik and Min (2020) detected greenhouse gas influence in the observed decrease of early spring SCE between 1925 and 2019, which was found to be separable from the responses to other forcings.

In summary, it is very likely that anthropogenic influence contributed to the observed reductions in Northern Hemisphere springtime snow cover since 1950. CMIP6 models better represent the seasonality and geographical distribution of snow cover than CMIP5 simulations (high confidence). Both CMIP5 and CMIP6 models simulate strong declines in spring SCE during recent years, in general agreement with observations, causing the multi-model mean decreasing trend in spring SCE to now better agree with observations than in earlier evaluations. Evidence has yet to emerge that interactions between vegetation and snow, found problematic in CMIP5, have improved in CMIP6 models (Section 9.5.3). Such deficiencies in the representation of snow in climate models mean there is medium confidence in the simulation of snow cover over the northern continents in CMIP6 model simulations. The models consistently link snow extent to surface air temperature (Figure 9.24). With warming of near-surface air linked to anthropogenic influence, and particularly to greenhouse gas increases (Section 3.3.1.1), this provides additional evidence that reductions in snow cover are also caused by human activity.

Glaciers are defined as perennial surface land ice masses independent of the Antarctic and Greenland Ice Sheets (Sections 9.5 and 2.3.2.3). The AR5 assessed that anthropogenic influence had likely contributed to the retreat of glaciers observed since the 1960s (Bindoff et al., 2013), based on a high level of scientific understanding and robust estimates of observed mass loss, internal variability, and glacier response to climatic drivers. The SROCC (Hock et al., 2019b) concluded that atmospheric warming was very likely the primary driver of glacier recession.

A regional study considering 85 Northern Hemisphere glacier systems concluded that there is a discernible human influence on glacier mass balance, with glacier model simulations driven by CMIP5 historical and greenhouse gas-only simulations showing a glacier mass loss, whereas those driven by natural-only forced simulations showed a net glacier growth (Hirabayashi et al., 2016). In addition, a study of the role of climate change in glacier retreat using a simple mass-balance model for 37 glaciers worldwide, concluded that observed length changes would not have occurred without anthropogenic climate change, with observed length variations exceeding those associated with internal variability by several standard deviations in many cases (Roe et al., 2017). Roe et al. (2021) used the same model to estimate that at least 85% of cumulative glacier mass loss since 1850 is attributable to anthropogenic influence. While Marzeion et al. (2014) found that anthropogenic influence contributed only 25 ± 35% of glacier mass loss for the period 1851–2010, their naturally-forced simulations exhibited a substantial negative mass balance, which Roe et al. (2021) argued is unrealistic. Moreover, Marzeion et al. (2014) estimated that anthropogenic influence contributed 69 ± 24% of glacier mass loss for the period 1991 to 2010, consistent with a progressively increasing fraction of mass loss attributable to anthropogenic influence found by Roe et al. (2021).

In summary, considering together the SROCC assessment that atmospheric warming was very likely the primary driver of glacier recession, the results of Roe et al. (2017, 2021) and our assessment of the dominant role of anthropogenic influence in driving atmospheric warming (Section 3.3.1), we conclude that human influence is very likely the main driver of the near-universal retreat of glaciers globally since the 1990s.

The AR5 assessed that it is likely that anthropogenic forcing contributed to the surface melting of the Greenland Ice Sheet since 1993 (Bindoff et al., 2013). The SROCC did not directly assess the attribution of Greenland Ice Sheet change to anthropogenic forcing, but it did assess with medium confidence that summer melting of the Greenland Ice Sheet has increased to a level unprecedented over at least the last 350 years, which is two-to-fivefold the pre-industrial level (see also Trusel et al., 2018).

Detection and attribution studies of change in the Greenland Ice Sheet remain challenging (Kjeldsen et al., 2015; Bamber et al., 2019). This is in part due to the short observational record (Shepherd et al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018; Mouginot et al., 2019; Rignot et al., 2019) and the challenges this poses to the evaluation of modelling efforts (Section 9.4.1.2). The latter require not only dynamic ice-sheet models, but also appropriate atmospheric and oceanic conditions to use as a boundary forcing to drive the models (Nowicki and Seroussi, 2018; Barthel et al., 2020). Nonetheless, new literature since AR5 finds that ice-sheet mass balance calculations using reanalysis-driven regional model simulations of surface mass balance are found to agree well with the observed decrease in ice-sheet mass over the past twenty years (Fettweis et al., 2020; Sasgen et al., 2020; Tedesco and Fettweis, 2020), consistent with earlier studies (Flato et al., 2013). These studies also show that the exceptional melt events observed in 2012 and 2019 were associated with exceptional atmospheric conditions (Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results support the finding that increased surface melting is associated with warming, although atmospheric circulation anomalies, including the summer North Atlantic Oscillation (NAO) and variations in snowfall play an important role in driving interannual variations (Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020). Further, a coupled ice-sheet-climate model study found emergence of decreased surface mass balance prior to the present day in coastal locations in Greenland, which dominate the integrated surface mass balance (Fyke et al., 2014), suggesting that observed variations in surface mass balance in these regions might be expected to be distinguishable from internal variability. A CMIP6 simulation of the historical period showed stable Greenland surface mass balance up to the 1990s, after which it declined due to increased melt and runoff, consistent with a downscaled reanalysis (van Kampenhout et al., 2020). Further, all experts surveyed in a structured expert judgement exercise examining the causes of the increase in mass loss from the Greenland Ice Sheet over the last two decades (Bamber et al., 2019) concluded that external forcing was responsible for at least 50% of the mass loss. A comparison of Greenland Ice Sheet mass loss trends from observations and AR5 model projections for the period 2007–2017 found that the magnitude of the observed surface mass balance trends was at the top of the AR5 assessed range, while mass loss due to changing ice dynamics was near the centre of the AR5 range (Slater et al., 2020), providing further evidence of consistent anthropogenically-forced mass loss trends in models and observations.

Drawing together the evidence from the continued and strengthened observed mass loss, the agreement between anthropogenically forced climate simulations and observations, and historical and paleo evidence for the unusualness of the observed rate of surface melting and mass loss, we assess that it is very likely that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades, and that there is medium confidence in an anthropogenic contribution to recent overall mass loss from Greenland.

AR5 assessed that there was low confidence in attributing the causes of the observed mass loss from the Antarctic Ice Sheet since 1993 (Bindoff et al., 2013). The SROCC assessed that there is medium agreement but limited evidence of anthropogenic forcing of Antarctic mass balance through both surface mass balance and glacier dynamics. It further assessed that Antarctic ice loss is dominated by acceleration, retreat and rapid thinning of the major West Antarctic Ice Sheet outlet glaciers (very high confidence), driven by melting of ice shelves by warm ocean waters (high confidence). Based on updated observations, Chapter 2 assesses that there is very high confidence that the Antarctic Ice Sheet lost mass between 1992 and 2017, and that there is medium confidence that this mass loss has accelerated. Models of Antarctic Ice Sheet evolution are evaluated in detail in Section 9.4.2.2, which assesses that there is medium confidence in many ice-sheet processes in Antarctic Ice Sheet models, but low confidence in the ocean forcing affecting basal melt rates. CMIP5 and CMIP6 models perform similarly in their simulation of Antarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020). Model evaluation of surface mass balance over the Antarctic Ice Sheet, including regional aspects, is also assessed in Atlas.11.1.3.

Ice discharge around the West Antarctic Ice Sheet is strongly influenced by variability in basal melt (Jenkins et al., 2018; Hoffman et al., 2019), in particular at decadal and longer time scales (Snow et al., 2017). Basal melt rate variability can be induced by wind-driven ocean current changes, which may partly be of anthropogenic origin via greenhouse gas forcing (Holland et al., 2019). Moreover, ice discharge losses from the Antarctic Ice Sheet over the 2007–2017 period are close to the centre of the model-based range projected in AR5 (Slater et al., 2020). However, expert opinion differs as to whether recent Antarctic ice loss from the West Antarctic Ice Sheet has been driven primarily by external forcing or by internal variability, and there is no consensus (Bamber et al., 2019). Anthropogenic influence on the Antarctic surface mass balance, which is expected to partially compensate for ice discharge losses through increases in snowfall, is currently masked by strong natural variability (Previdi and Polvani, 2016; Bodart and Bingham, 2019), and observations suggest that it has been close to zero over recent years (see further discussion in Section 9.4.2.1; Slater et al., 2020).

Overall, there is medium agreement but limited evidence of anthropogenic influence on Antarctic mass balance through changes in ice discharge.

The global ocean plays an important role in the climate system, as it is responsible for transporting and storing large amounts of heat (Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the atmosphere. Therefore, accurate ocean simulation in climate models is essential for the realistic representation of the climatic response to anthropogenic warming, including rates of warming, sea level rise and carbon uptake, and the representation of coupled modes of climate variability.

The ocean plays an important role as the Earth’s primary energy store. The AR5 and SROCC assessed that the ocean accounted for more than 90% of the Earth’s energy change since the 1970s (Rhein et al., 2013; Bindoff et al., 2019). These assessments are consistent with recent studies assessed in Section 7.2 and Cross-Chapter Box 9.1, which find that 91% of the observed change in Earth’s total energy from 1971 to 2018 was stored in the ocean (von Schuckmann et al., 2020). The AR5 concluded that anthropogenic forcing has very likely made a substantial contribution to ocean warming above 700 m, whereas below 700 m, limited measurements restricted the assessment of ocean heat content changes in AR5 and prevented a robust comparison between observations and models (Bindoff et al., 2013).

The multi-model means of both CMIP5 and CMIP6 historical simulations forced with time varying natural and anthropogenic forcing shows robust increases in ocean heat content in the upper (0–700 m) and intermediate (700–2000 m) ocean (high confidence) (Figure 3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface and subsurface cooling during and after large volcanic eruptions is also captured in the upper-ocean, and global mean ocean heat content (Balmaseda et al., 2013). The ocean heat content increase is also reflected in the corresponding ocean thermal expansion which is a leading contributor to global mean sea level rise (Sections 3.5.3.2 and 9.2.4, and Box 9.1).

Since AR5, the attribution of ocean heat content increases to anthropogenic forcing has been further supported by more detection and attribution studies. These studies have shown that contributions from natural forcing alone cannot explain the observed changes in ocean heat content in either the upper or intermediate ocean layers, and a response to anthropogenic forcing is clearly detectable in ocean heat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Moreover, a response to greenhouse gas forcing is detectable independently of the response to other anthropogenic forcings (Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of the greenhouse gas induced warming. Further evidence is provided by the agreement between observed and simulated changes in global thermal expansion associated with the ocean heat content increase when both natural and anthropogenic forcings are included in the simulations (Section 3.5.3.2), though internal variability plays a larger role in driving basin-scale thermosteric sea level trends (Bilbao et al., 2015). Over the Southern Ocean, warming is detectable over the late 20th century and is largely attributable to greenhouse gases (Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic forcings such as ozone depletion have been shown to mitigate the warming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs et al., 2021). The use of the mean temperature above a fixed isotherm rather than fixed depth further strengthens a robust detection of the anthropogenic response in the upper ocean (Weller et al., 2016), and better accounting for internal variability in the upper ocean (Rathore et al., 2020), helps explain reported hemispheric asymmetry in ocean heat content change (Durack et al., 2014b).

While ocean assessments have primarily focused on temperature changes, improved observational salinity products since the early 2000s have supported more assessment of long-term ocean salinity change and variability from AR4 (Bindoff et al., 2007) to AR5 across both models and observations (Flato et al., 2013; Rhein et al., 2013). The AR5 assessed that it was very likely that anthropogenic forcings have made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s. The SROCC augmented these insights, noting that observed high latitude freshening and warming have very likely made the surface ocean less dense with a stratification increase of between 2.18% and 2.42% from 1970 to 2017 (Bindoff et al., 2019). A recent observational analysis has expanded on these assessments, suggesting a very marked summertime density contrast enhancement across the mixed layer base of 6.2–11.6% per decade, driven by changes in temperature and salinity, which is more than six times larger than previous estimates (Sallée et al., 2021). An idealized ocean modelling study suggests that the enhanced stratification can account for a third of the salinity enhancement signal since 1990 (Zika et al., 2018). Thus, there has been an expansion of observed global- and basin-scale salinity change assessment literature since AR5, with many new studies reproducing the key patterns of long-term salinity change reported in AR5 (Rhein et al., 2013), and linking these through modelling studies to coincident changes in evaporation–precipitation patterns at the ocean surface (Sections 2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).

AR5 concluded that it was very likely that anthropogenic forcings had made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s (Bindoff et al., 2013; Rhein et al., 2013). It highlighted that the spatial patterns of salinity trends, and the mean fields of salinity and evaporation minus precipitation are all similar, with an enhancement to Atlantic Ocean salinity and freshening in the Pacific and Southern Oceans. Since AR5 all subsequent work on assessing observed and modelled salinity changes has confirmed these results.

Climate change detection and attribution studies have considered salinity, with the first of these assessed in AR5 (Bindoff et al., 2013). Since that time, the positive detection conclusions (Stott et al., 2008; Pierce et al., 2012; Terray et al., 2012) have been supported by a number of more recent and independent assessments which have reproduced the multi-decadal basin-scale patterns of change in observations and models (Figures 3.27 and 3.28; Durack et al., 2014a; Durack, 2015; Levang and Schmitt, 2015; Skliris et al., 2016). Observed depth-integrated basin responses, contrasting the Pacific and Atlantic basins (freshening Pacific and enhanced salinity Atlantic) were also shown to be replicated in most historical (natural and anthropogenically forced) simulations, with this basin contrast absent in CMIP5 and CMIP6 natural-only simulations that exclude anthropogenic forcing (Durack et al., 2014a; Figure 3.28).

The SROCC concluded with high confidence that the dominant cause of GMSL rise since 1970 is anthropogenic forcing. Prior to that, AR5 had concluded that it is very likely that there has been a substantial contribution from anthropogenic forcings to GMSL rise since the 1970s. Since AR5, several studies have identified a human contribution to observed sea level change resulting from a warming climate as manifest in thermosteric sea level change and the contribution from melting glaciers and ice sheets.

For the global mean thermosteric sea level change, Slangen et al. (2014) showed the importance of anthropogenic forcings (combined greenhouse gas and aerosol forcings) for explaining the magnitude of the observed changes between 1957 and 2005, considering the full depth of the ocean and natural forcings in order to capture the variability (see also Figure 3.29). Over the 1950–2005 period, Marcos and Amores (2014) found that human influence explains 87% of the 0–700 m global thermosteric sea level rise. Both thermosteric and regional dynamic patterns of sea level change in individual forcing experiments from CMIP5 were considered by Slangen et al. (2015) who showed that responses to anthropogenic forcings are significantly different from both internal variability and inter-model differences and that although greenhouse gas and anthropogenic aerosol forcings produce opposite GMSL responses, there are differences in the response on regional scales. Based on these studies, we conclude that it is very likely that anthropogenic forcing was the main driver of the observed global mean thermosteric sea level change since 1970.

In an attribution study of the sea-level contributions of glaciers, Marzeion et al. (2014) found that between 1991 and 2010, the anthropogenic fraction of global glacier mass loss was 69 ± 24% (see also Section 3.4.3.1). Slangen et al. (2016) considered all quantifiable components of the GMSL budget and showed that anthropogenically forced changes account for 69 ± 31% of the observed sea level rise over the period 1970 to 2005, whereas natural forcings combined with internal variability have a much smaller effect – only contributing 9 ± 18% of the change over the same period. These studies indicate that about 70% of the combined change in glaciers, ice-sheet surface mass balance and thermal expansion since 1970 can be attributed to anthropogenic forcing, and that this percentage has increased over the course of the 20th century. Detection studies on GMSL change in the 20th century (Becker et al., 2014; Dangendorf et al., (2015) found that observed total GMSL change in the 20th century was inconsistent with internal variability. Dangendorf et al. (2015) determined that for 1900 to 2011 at least 45% of GMSL change is human-induced. A study that developed a semi-empirical model to link sea-level change to observed GMST change concluded that at least 41% of the 20th century sea-level rise would not have happened in the absence of the century’s increasing GMST and that there was a 95% probability that by 1970 GMSL was higher than that which would have occurred in the absence of increasing GMST (Kopp et al., 2016). Richter et al. (2020) compared modelled sea level change with the satellite altimeter observations from 1993 to 2015; a period short enough that internal variability can dominate the spatial pattern of change. They found that when GMSL is not removed, model simulated zonally averaged sea level trends are consistent with altimeter observations globally as well as in each ocean basin and much larger than might be expected from internal variability. Using spatial correlation, Fasullo and Nerem (2018) showed that the satellite altimeter trend pattern is already detectable.

We note that current detection and attribution studies do not yet include all processes that are important for sea-level change (see Section 9.6). However, based on the body of literature available, we conclude that the main driver of the observed GMSL rise since at least 1971 is very likely anthropogenic forcing. The assessed period starts in 1971 for consistency with observations assessed in Cross-Chapter Box 9.1.

Circulation of the ocean, whether it be wind or density driven, plays a prominent role in the heat and freshwater transport of the Earth system (Buckley and Marshall, 2016). Thus, its accurate representation is crucial for the realistic representation of water mass properties, and replication of observed changes driven by atmosphere-land-ocean coupling. Here, we assess the ability of CMIP models to reproduce the observed large-scale ocean circulation, along with assessment of the detection and attribution of any anthropogenically-driven changes. We also note that the process-based understanding of these circulation changes and circulation changes occurring at smaller scales is assessed in Section 9.2.3.

The AR5 concluded that while climate models suggested that an AMOC slowdown would occur in response to anthropogenic forcing, the short direct observational AMOC record precluded it from being used to support this model finding. Chapter 2 reports with high confidence, a weakening of the AMOC was observed in the mid-2000s to the mid-2010s, while again also noting that the observational record was too short to determine whether this is a significant trend or a manifestation of decadal and multi-decadal variability (Section 2.3.3.4.1). Indirect evidence of AMOC weakening since at least the 1950s is also presented, but confidence in this longer-term decrease was low Section 2.3.3.4.1).

As reported in Section 2.3.3.4.1, estimates of AMOC since at least 1950, which are generated from observed surface temperatures or sea surface height, suggest the AMOC weakened through the 20th century (low confidence) (Ezer et al., 2013; Caesar et al., 2018). Over the same period, the CMIP5 multi-model mean showed no significant net forced response in AMOC (Cheng et al., 2013). However, a significant forced change is simulated in the CMIP6 multi-model mean, where a clear increase of the AMOC is seen over the 1940–1985 period (Figure 3.30e; Menary et al., 2020). Although there is general agreement that the influence of greenhouse gases acts to a weaken the modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018), changes in solar, volcanic and anthropogenic aerosol emissions can lead to temporary changes in AMOC on decadal- to multi-decadal time scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary and Scaife, 2014; Swingedouw et al., 2017; Undorf et al., 2018b). As such, the simulated net forced response in AMOC is a balance between the different forcing factors (Section 9.2.3.1; Delworth and Dixon, 2006; Menary et al., 2020). The differing AMOC response of CMIP5 and CMIP6 models during the historical period has been associated with stronger aerosol effective radiative forcing in the CMIP6 models (Menary et al., 2020), such that the aerosol-induced AMOC increase during the 1940–1985 period overcomes the greenhouse gas induced decline (Figure 3.30e). However, models simulate a range of anthropogenic aerosol effective radiative forcing and a range of historical AMOC trends in CMIP6 (Menary et al., 2020) and there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century (Figure 3.30d–f) due to disagreement among the differing lines of evidence. For example, ocean reanalysis (Jackson et al., 2019) and forced ocean model simulations (Robson et al., 2012; Danabasoglu et al., 2016), which show AMOC changes that are broadly consistent with the CMIP6 response, appear to disagree with observational estimates of AMOC over the historical period (Ezer et al., 2013; Caesar et al., 2018). It is noted, however, that the relatively short length of the forced ocean simulations and ocean reanalysis precludes a comparable assessment of 20th century trends. Furthermore, despite the similar AMOC evolution seen in forced ocean model simulations and the CMIP6 models, it is unclear whether the same underlying mechanisms are responsible for the changes.

In summary, models do not support robust assessment of the role of anthropogenic forcing in the observed AMOC weakening between the mid-2000s and the mid-2010s, which is assessed to have occurred with high confidence in Section 2.3.3.4.1, as the changes are outside of the range of modelled AMOC trends (regardless of whether they are forced or internally generated) in most models. Thus, we have low confidence that anthropogenic forcing has influenced the observed changes in AMOC strength in the post-2004 period. In addition, there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century due to disagreement among the differing lines of observational and modelled evidence (i.e., historical AMOC estimates, ocean reanalysis, forced ocean simulations and historical CMIP6 simulations). Thus, we have low confidence that anthropogenic forcing has had a significant influence on changes in AMOC strength during the 1860–2014 period.

Proposed causes of the trend in the amplitude of the seasonal cycle of CO₂, and its amplification at higher latitudes, include increases in the summer productivity and/or increases in the magnitude of winter respiration of northern ecosystems (Barichivich et al., 2013; Graven et al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in productivity throughout the Northern Hemisphere by CO₂ fertilization, and increases in the productivity of agricultural crops in northern mid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies have attempted to quantify the different contributions by comparing atmospheric CO₂ observations with ensembles of land surface model simulations. Piao et al. (2017) found that CO₂ fertilization of photosynthesis is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO₂ but noted that climate change drives the latitudinal differences in that increase. North of 40°N, Bastos et al. (2019) also found CO₂ fertilization to be the most likely driver, with warming at northern high latitudes contributing a decrease in amplitude, in contrast to earlier conclusions (Graven et al., 2013; Forkel et al., 2016), and agricultural and land use changes making only a small contribution. For temperate regions of the Northern Hemisphere, K. Wang et al. (2020) found that the importance of CO₂ fertilization is decreased by drought stress, but also found only a small contribution from agricultural and land use changes. However, many global models do not include nitrogen fertilization, changes to crop cultivars or irrigation effects, with the latter associated with deficiencies in simulated terrestrial water cycling (H. Yang et al., 2018). All these factors influence the capability of models to simulate accurately the seasonal cycle in atmosphere-land CO₂ exchanges. Model comparisons to the atmospheric CO₂ concentration record for Barrow, Alaska, suggest that models underestimate current levels of carbon fixation (Winkler et al., 2019) and have deficiencies in their phenological representation of greenness levels, particularly for autumn (Z. Li et al., 2018). Based on these studies and noting the uncertainty in the processes ultimately driving changes in atmospheric CO₂ seasonal cycles (Section 5.2.1.4), we assess with medium confidence that fertilization by anthropogenic increases in atmospheric CO₂ concentrations is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO₂.

Section 5.2.1.3 assesses that both observational reconstructions based on the partial pressure of CO₂ and ocean biogeochemical models show a quasi-linear increase in the ocean sink of anthropogenic CO₂ from 1.0 ± 0.3 PgC yr^–1 to 2.5 ± 0.6 PgC yr^–1 between 1960–1969 and 2010–2019 in response to global CO₂ emissions (high confidence). During the 1990s, the global net flux of CO₂ into the ocean is estimated to have weakened to 0.8 ± 0.5 PgC yr^–1 while in 2000 and thereafter, it is estimated to have strengthened considerably to rates of 2.0 ± 0.5 PgC yr^–1, associated with changes in SST, the surface concentration of dissolved inorganic carbon and alkalinity, and decadal variations in atmospheric forcing (Landschützer et al., 2016, see also Section 5.2).

Ocean acidification is one of the most detectible metrics of environmental change and was well covered in AR5, in which it was assessed that the uptake of anthropogenic CO₂ had very likely resulted in acidification of surface waters (Bindoff et al., 2013). Since then, observations and simulations of multi-decadal trends in surface carbon chemistry have increased in robustness. The evidence on ocean pH decline had further strengthened in SROCC with good agreement found between CMIP5 models and observations and an assessment that the ocean was continuing to acidify in response to ongoing carbon uptake (Bindoff et al., 2019). An observed decrease in global surface open ocean pH is assessed in Section 2.3.3.5 to be virtually certain to have occurred with a rate of 0.003–0.026 per decade for the past 40 years. The ocean acidification has occurred not only in the surface layer but also in the interior of the ocean (Sections 2.3.3.5 and 5.3.3). Rates have been observed to be between −0.015 and −0.020 per decade in mode and intermediate waters of the North Atlantic through the combined effect of increased anthropogenic and remineralized carbon (Ríos et al., 2015) and acidification has been observed down to 3000 m in the deep water formation regions (Perez et al., 2018). There has also been considerable improvement in detection and attribution of anthropogenic CO₂ versus eutrophication-based acidification in coastal waters (Wallace et al., 2014).

The increased evidence in recent studies supports an assessment that it is virtually certain that the uptake of anthropogenic CO₂ was the main driver of the observed acidification of the global surface open ocean. The observed increase in acidification over the North Atlantic subtropical and equatorial regions since 2000 is likely associated in part with an increase in ocean temperature, a response which corresponds to the expected weakening of the ocean carbon sink with warming. Due to strong internal variability, systematic changes in carbon uptake in response to climate warming have not been observed in most other ocean basins at present. We further assess, consistent with AR5 and SROCC, that deoxygenation in the upper ocean is due in part to anthropogenic forcing, with medium confidence. There is high confidence that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink.

Conclusions on external forcing influences on the NAM are supported by CMIP6 results based on single forcing ensembles (Figure 3.34a). Positive trends are found in historical simulations over 1958–2019 in boreal winter and are mainly driven by greenhouse gas increases. No significant trends are simulated in response to anthropogenic aerosols, stratospheric ozone or natural forcing. Albeit weak and not statistically significant, the sign of the multi-model mean forced response due to natural forcing is consistent with the observed reduction of solar activity since the 1980s (Section 2.2.1) whose influence would have favoured the negative phase of wintertime NAM/NAO based on the fingerprint of the nearly periodical 11-year solar cycle extracted from models (Scaife et al., 2013; Andrews et al., 2015; Thiéblemont et al., 2015) or observations (Gray et al., 2016; Lüdecke et al., 2020). But such an NAO response to solar forcing remains highly uncertain and controversial, being contradicted by longer proxy records over the last millennium (Sjolte et al., 2018) and modelling evidence (Gillett and Fyfe, 2013; Chiodo et al., 2019). For all seasons and for all individual forcings, uncertainties remain in the estimation of the forced response in the NAM trend as evidenced by considerable model spread (Figure 3.34a) and because the simulated forced component has small amplitude compared to internal variability.

In summary, CMIP5 and CMIP6 models are skilful in simulating the spatial features and the variance of the NAM/NAO and associated teleconnections (high confidence). There is limited evidence for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM/NAO from the mid 20th century. Confidence in attribution is low: (i) because there is a large spread in the modelled forced responses which is overwhelmed anyway by internal variability; (ii) because of the apparent signal-to-noise problem; and (iii) because of the chronic inability of models to produce a range of trends which encompasses the observed estimates over the last 60 years.

In general agreement with AR5, new research continues to indicate that both stratospheric ozone depletion and increasing greenhouse gases have contributed to the trend of the SAM during austral summer toward its positive phase in recent decades (Solomon and Polvani, 2016), with the ozone depletion influence dominating (Gerber and Son, 2014; Son et al., 2018). In CMIP6 historical simulations there are significant positive SAM trends over the 1979–2019 period in austral summer, although the contribution from ozone forcing evaluated with the four available models is not significant (Figure 3.34b). Three of these models share the same standard prescribed ozone forcing and produce significantly positive SAM trends over an extended period (1957–2019). The fourth model, MRI-ESM2-0, has the option of interactive ozone chemistry. Its ozone-only experiment is forced by prescribed ozone derived from its own historical simulations and produces a negative SAM trend associated with weak ozone depletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and Morgenstern (2021) find an indirect influence of greenhouse gases on the SAM via induced ozone changes in coupled chemistry-climate simulations, which differ from the prescribed ozone simulations shown in Figure 3.34b. Since about 1997, the effective abundance of ozone-depleting halogen has been decreasing in the stratosphere (WMO, 2018), leading to a stabilization or even a reversal of stratospheric ozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization and slight recovery since about 2000 may have caused a pause in the summertime SAM trend (Figure 3.34c; Saggioro and Shepherd, 2019; Banerjee et al., 2020), although some influence from internal variability cannot be ruled out. While some studies find an anthropogenic aerosol influence on the summertime SAM (Gillett et al., 2013; Rotstayn, 2013), recent studies with larger multi-model ensembles find that this effect is not robust (Steptoe et al., 2016; Choi et al., 2019), consistent with CMIP6 single forcing ensembles (Figure 3.34). In the CMIP5 simulations, volcanic stratospheric aerosol has a significant weakening effect on the SAM in autumn and winter (Cross-Chapter Box 4.1; Gillett and Fyfe, 2013), but there is no evidence that this effect leads to a significant multi-decadal trend since the late 20th century. Beyond external forcing, Fogt et al. (2017) show a significant association of tropical SST variability with the summertime SAM trend since the mid-20th century in agreement with Lim et al. (2016), who, however, demonstrate that such a teleconnection between the summertime SAM and El Niño–Southern Oscillation (Annex IV.2.3), found in observations, is missing in many CMIP5 models.

In summary, it is very likely that anthropogenic forcings have contributed to the observed trend of the summer SAM toward its positive phase since the 1970s. This assessment is supported by further model studies that confirm the human influence on the summertime SAM with improved models since AR5. While ozone depletion contributed to the trend from the 1970s to the 1990s (medium confidence), its influence has been small since 2000, leading to a weaker summertime SAM trend over 2000–2019 (medium confidence). Climate models reproduce the spatial structure of the summertime SAM observed since the late 1970s well (high confidence). CMIP6 models reproduce the spatiotemporal features and recent multi-decadal trend of the summertime SAM better than CMIP5 models (medium confidence). However, there is a large spread in the intensity of the SAM response to ozone and greenhouse gas changes in both CMIP5 and CMIP6 models (high confidence), which limits the confidence in the assessment of the ozone contribution to the observed trends. CMIP5 and CMIP6 models do not capture multicentennial variability of the SAM found in proxy reconstructions (low confidence). This confidence level reflects that it is unclear whether this is due to a model or an observational shortcoming.

To conclude, ENSO representation in CMIP5 models displayed a significant improvement from the representation of ENSO variability in CMIP3 models, which displayed much more intermodel spread in standard deviation, and stronger biennial periodicity (Guilyardi et al., 2012; Flato et al., 2013). In general, there has been no large step change in the representation of ENSO between CMIP5 and CMIP6, however, CMIP6 models appear to better represent some key ENSO characteristics (e.g., Brown et al., 2020; Planton et al., 2021). The instrumental record and paleo-proxy evidence through the Holocene all suggest that ENSO can display considerable modulations in amplitude, pattern and period (see also (Section 2.4.2). For the period since 1850, there is no clear evidence for a sustained shift in ENSO index beyond the range of internal variability. However, paleo-proxy evidence indicates with medium confidence that ENSO variability since 1950 is greater than at any time between 1400 and 1850 (Section 2.4.2). Coupled models display large changes of ENSO behaviour in the absence of external forcing changes, and little-to-no variance sensitivity to historical anthropogenic forcing. Thus, there is low confidence that anthropogenic forcing has led to the changes of ENSO variability inferred from paleo-proxy evidence.

Chapter 2 reports low confidence that the apparent change from East Pacific- to Central Pacific-type El Niño events that occurred in the last 20–30 years was representative of a long term change. While some climate models do suggest external forcing may affect the El Niño event type, most climate models suggest that what has been observed is well within the range of natural variability. Thus, there is low confidence that anthropogenic forcing has had an influence on the observed changes in El Niño event type.

The observed Indian Ocean basin-average SST increase on multi-decadal and centennial time scales is well represented by CMIP5 historical simulations, and has been attributed to the effects of greenhouse gases offset in part by the effects of anthropogenic aerosols mainly through aerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b). The observed SST trend is larger in the western than eastern tropical Indian Ocean, which leads to an apparent upward trend of the IOD index, but this trend is statistically insignificant (Section 2.4.3). CMIP5 models capture this warming pattern, which may be associated with Walker circulation weakening over the Indian Ocean due to greenhouse gas forcing (Dong and Zhou, 2014). However, strong internal decadal IOD-like variability and observational uncertainty preclude attribution (Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such a positive IOD-like change in equatorial zonal SST gradient suggests an increase in the frequency of extreme positive events (Cai et al., 2014) and skewness (Cowan et al., 2015) of the IOD mode. While there is some evidence of an increase in frequency of positive IOD events during the second half of the 20th century, the current level of IOD variability is not unprecedented in a proxy reconstruction for the last millennium (Section 2.4.3; Abram et al., 2020). Besides, the IOD magnitude in the late 20th century is not significantly different between CMIP5 simulations forced by historical and natural-only forcings, though this conclusion is based on only five selected ensemble members that realistically reproduce statistical features of the IOD (Blau and Ha, 2020). While selected CMIP5 models show weakening (Thielke and Mölg, 2019) and seasonality changes (Blau and Ha, 2020) in IOD-induced rainfall anomalies in tropical eastern Africa, no comparison with observational records has been made. Likewise, while a strengthening tendency of the ENSO-IOB mode correlation and resultant intensification of the IOB mode are found in historical or future simulations in selected CMIP5 models (Hu et al., 2014; Tao et al., 2015), such a change has not been detected in observational records.

After linear detrending, Pacific Decadal Variability (PDV; Annex IV.2.6; Section 3.7.6) has been suggested as a driver of decadal to multi-decadal variations in the IOB mode (Dong et al., 2016). However, correlation between the PDV and a decadal IOB index, defined from linearly detrended SST, changed from positive to negative during the 1980s (Han et al., 2014a). The increase in anthropogenic forcing and recovery from the eruptions of El Chichón in 1982 and Pinatubo in 1991 may have overwhelmed the PDV influence, and explain this change (Dong and McPhaden, 2017; L. Zhang et al., 2018a). However, the low statistical degrees of freedom hamper clear detection of human influence in this correlation change.

To summarize, there is medium confidence that changes in the interannual IOD variability in the late 20th century inferred from observations and proxy records are within the range of internal variability. There is no evidence of anthropogenic influence on the interannual IOB. On decadal- to multi-decadal time scales, there is low confidence that human influence has caused a reversal of the correlation between PDV and decadal variations in the IOB mode. The low confidence in this assessment is due to the short observational record, limited number of models used for the attribution, lack of model evaluation of the decadal IOB mode, and uncertainty in the contribution from volcanic aerosols. Nevertheless, CMIP5 models have medium overall performance in reproducing both the interannual IOB and IOD modes, with an apparently good performance in reproducing the IOB magnitude arising from compensation of biases in the formation process, and overly high IOD magnitude due to the mean state bias (high confidence). There is no clear improvement in the simulation of the IOD from CMIP5 to CMIP6 models, though there is only medium confidence in this assessment, since only a subset of CMIP6 models have been examined. There is no evidence for performance changes in simulating the IOB from CMIP5 to CMIP6 models.

(Section 2.4.4 assess that there is low confidence in any sustained changes to the AZM and AMM variability in instrumental observations. Moreover, any attribution of possible human influence on the Atlantic modes and associated teleconnections is limited by the poor fidelity of CMIP5 and CMIP6 models in reproducing the mean tropical Atlantic climate, its seasonality and variability, despite hints of some improvement in CMIP6, as well as other sources of uncertainties related to limited process understanding in the observations (Foltz et al., 2019), the response of the tropical Atlantic climate to anthropogenic aerosol forcing (Booth et al., 2012; Zhang et al., 2013a) and the presence of strong multi-decadal fluctuations related to AMV (Section 3.7.7) and cross-tropical basin interactions (Martín-Rey et al., 2018; Cai et al., 2019). The fact that most models poorly represent the climatology and variability of the tropical Atlantic combined with the short observational record makes it difficult to place the recent observed changes in the context of internal multi-annual variability versus anthropogenic forcing.

In summary, based on CMIP5 and CMIP6 results, there is no robust evidence that the observed changes in either the Atlantic Niño or AMM modes and associated teleconnections over the second half of the 20th century are beyond the range of internal variability or have been influenced by natural or anthropogenic forcing. Considering the physical processes responsible for model biases in these modes, increasing resolution in both ocean and atmosphere components may be an opportunity for progress in the simulation of the tropical Atlantic changes as evidenced by some individual model studies (Roberts et al., 2018), but this needs confirmation from a multi-model perspective.

While PDV is primarily understood as an internal mode of variability (Si and Hu, 2017), there are some indications that anthropogenically induced SST changes project onto PDV and have contributed to its past evolution (Bonfils and Santer, 2011; Dong et al., 2014a; Boo et al., 2015; Xu and Hu, 2018). However, the level of evidence is limited because of the difficulty in correctly separating internal versus externally forced components of the observed SST variations, and because it is unclear whether the dynamics of the PDV are operative in this forced SST change pattern. Over the last two to three decades which encompass the period of slower GMST increase (Cross-Chapter Box 3.1), Smith et al. (2016) found that anthropogenic aerosols have driven part of the PDV change toward its negative phase. A similar result is shown in Takahashi and Watanabe (2016) who found intensification of the Pacific Walker circulation in response to aerosol forcing (Section 3.3.3.1.2). Indeed, CMIP6 models simulate a negative PDV trend since the 1980s (Figure 3.39f), which is much weaker than internal variability. However, a response to anthropogenic aerosols is not robustly identified in a large ensemble of a model (Oudar et al., 2018), across CMIP5 models (Hua et al., 2018), or in idealized model simulations (Kuntz and Schrag, 2016). Alternatively, inter-basin teleconnections associated with the warming of the North Atlantic Ocean related to the mid-1990s phase shift of the AMV (McGregor et al., 2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), and also warming in the Indian Ocean (Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV transition to its negative phase in the 2000s. Considering the possible influence of external forcing on Indian Ocean decadal variability (Section 3.7.4) and AMV (Section 3.7.7), any such human influence on PDV would be indirect through changes in these ocean basins, and then imported to the Pacific via inter-basin coupling. However, this human influence on AMV, and how consistently such inter-basin processes affect PDV phase shifts, are uncertain. Other modelling studies find that anthropogenic aerosols can influence the PDV (Verma et al., 2019; Amiri-Farahani et al., 2020; Dow et al., 2020). It is however unclear whether and how much those forcings contributed to the observed variations of PDV. In CMIP6 models, the temporal correlation of the multi-model ensemble mean PDV index with its observational counterpart is insignificant and negligible (Figure 3.39f), suggesting that any externally-driven component in historical PDV variations was weak. Lastly, the multi-model ensemble mean computed from CMIP6 historical simulations shows slightly stronger variation than the CMIP5 counterpart, suggesting a greater simulated influence from external forcings in CMIP6. Still, the fraction of the forced signal to the total PDV is very low (Figure 3.39f), in contrast to AMV (Section 3.7.7). Consistently, Liguori et al. (2020) estimate that the variance fraction of the externally-driven to total PDV is up to only 15% in a multi-model large ensemble of historical simulations. These findings support an assessment that PDV is mostly driven by internal variability since the pre-industrial era. The sensitivity of ensemble-mean PDV trends to the ensemble size (Oudar et al., 2018), and the dominance of the ensemble spread over the ensemble mean in the 60-year trend of the equatorial Pacific zonal SST gradient in large ensemble simulations (Watanabe et al., 2021), also support this statement.

AR5 assessed, based on climate models, that the AMV was primarily internally-driven alongside some contribution from external forcings (mainly anthropogenic aerosols) over the late 20th century (Bindoff et al., 2013; Flato et al., 2013). But AR5 also concluded that models show medium performance in reproducing the observed AMV, with difficulties in simulating the time scale, the spatial structure and the coherency between all the physical processes involved (Flato et al., 2013).

There is additional evidence since AR5 that external forcing has been playing an important role in shaping the timing and intensity of the observed AMV since pre-industrial times (Bellomo et al., 2018; Andrews et al., 2020). The time synchronisation between observed and multi-model mean AMV SST indices is significant in both CMIP5 and CMIP6 historical simulations, while the explained variance of the forced response in CMIP6 appears stronger (Figure 3.40d–f). The competition between greenhouse gas warming and anthropogenic sulphate aerosol cooling has been proposed to be particularly important over the latter half of the 20th century (Booth et al., 2012; Steinman et al., 2015; Murphy et al., 2017; Undorf et al., 2018a; Haustein et al., 2019). The latest observed AMV shift from the cold to the warm phase in the mid-1990s at the surface ocean is well captured in the CMIP6 forced component and may be associated with the lagged response to increased AMOC due to strong anthropogenic aerosol forcing over 1955–1985 (Menary et al., 2020) in combination with the rapid response through surface flux processes to declining aerosol forcing and increasing greenhouse gas influence since then. However, natural forcings may have also played a significant role. For instance, volcanic forcing has been shown to contribute in part to the cold phases of the AMV-related SST anomalies observed in the 20th century (Terray, 2012; Bellucci et al., 2017; Swingedouw et al., 2017; Birkel et al., 2018). Over the last millennium, natural forcings including major volcanic eruptions and fluctuations in solar activity are thought to have driven a larger fraction of the multi-decadal variations in the AMV than in the industrial era, with some interplay with internal processes (Otterå et al., 2010; Knudsen et al., 2014; Moffa-Sánchez et al., 2014; J. Wang et al., 2017; Malik et al., 2018; Mann et al., 2021), but other studies question the role of natural forcings over this period (Zanchettin et al., 2014; Lapointe et al., 2020).

Model evaluation of the AMV phenomenon remains difficult because of short observational records (especially of detailed process-based observations), the lack of stationarity in the variance, spatial patterns and frequency of the AMV assessed from modelled SST (Qasmi et al., 2017), difficulties in estimating the forced signals in both historical simulations and observations (Tandon and Kushner, 2015), and because of probable interplay between internally and externally-driven processes (Watanabe and Tatebe, 2019). Furthermore, models simulate a large range of historical anthropogenic aerosol forcing (Smith et al., 2020) and questions often referred to as signal-to-noise paradox have been raised concerning the models’ ability to correctly simulate the magnitude of the response of AMV-related atmospheric circulation phenomena, such as the NAO (Section 3.7.1), to both internally and externally generated changes (Scaife and Smith, 2018). Related methodological and epistemological uncertainties also call into question the relevance of the traditional basin-average SST index to assessing the AMV phenomenon (Zanchettin et al., 2014; Frajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019).

To summarize, results from CMIP5 and CMIP6 models together with new statistical techniques to evaluate the forced component of modelled and observed AMV, provide robust evidence that external forcings have modulated AMV over the historical period. In particular, anthropogenic and volcanic aerosols are thought to have played a role in the timing and intensity of the negative (cold) phase of AMV recorded from the mid-1960s to mid-1990s and subsequent warming (medium confidence). However, there is low confidence in the estimated magnitude of the human influence. The limited level of confidence is primarily explained by difficulties in accurately evaluating model performance in simulating AMV. The evaluation is severely hampered by short instrumental records but also, equally importantly, by the lack of detailed and coherent long-term process-based observations (for example of the AMOC, aerosol optical depth, surface fluxes and cloud changes), which limit our process understanding. In addition, studies often rely solely on simplistic SST indices that may be hard to interpret (Zhang et al., 2016) and may mask critical physical inconsistencies in simulations of the AMV compared to observations (Zhang, 2017).

The AR5 concluded that human influence on the climate system is clear (IPCC, 2013), based on observed increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. The AR5 also assessed that it was virtually certain that internal variability alone could not account for observed warming since 1951 (Bindoff et al., 2013). Evidence has grown since AR5 that observed changes since the 1950s in many parts of the climate system are attributable to anthropogenic influence. So far, this chapter has focused on examining individual aspects of the climate system in separate sections. The results presented in Sections 3.3 to 3.7 substantially strengthen our assessment of the role of human influence on climate since pre-industrial times. In this section we look across the whole climate system to assess to what extent a physically consistent picture of human induced change emerges across the climate system (Figure 3.41).

The observed global surface air temperature warming of 0.9°C to 1.2°C in 2010–2019 is much greater than can be explained by internal variability (likely –0.2°C to +0.2°C) or natural forcings (likely –0.1°C to +0.1°C) alone, but consistent with the assessed anthropogenic warming (likely 0.8°C to 1.3°C; Section 3.3.1.1). It is very likely that human influence is the main driver of warming over land Section 3.3.1.1). Moreover, the atmosphere as a whole has warmed (Table 7.1), and it is very likely that human-induced greenhouse gas increases were the main driver of tropospheric warming since 1979 (Section 3.3.1.2). It is virtually certain that greenhouse gas forcing was the main driver of the observed changes in hot and cold extremes over land at the global scale (Cross-Chapter Box 3.2).

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Essential input to the simulations assessed here is provided by future scenarios of concentrations or anthropogenic emissions of radiatively active substances; the scenarios represent possible sets of decisions by humanity, without any assessment that one set of decisions is more probable to occur than any other set (Section 1.6). As in previous assessment reports, these scenarios are used for projections of future climate using global atmosphere–ocean general circulation models (AOGCMs) and Earth system models (ESMs; Section 1.5.3); the latter include representation of various biogeochemical cycles such as the carbon cycle, the sulphur cycle, or ozone (e.g., Flato, 2011; Flato et al., 2013). This chapter thus provides a comprehensive assessment of the future global climate response to different future anthropogenic perturbations to the climate system.

Studies focusing on the relationship of sea ice extent and changes in external drivers have consistently found a much-reduced likelihood of a practically ice-free Arctic Ocean during summer for global warming of 1.5°C than for 2.0°C (Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond et al., 2018; Olson et al., 2019). This is shown here in a large initial-condition ensemble of observationally constrained model simulations where GSAT are stabilized at 1.5°C, 2.0°C and 3.0°C warming relative to 1850–1900 in the RCP8.5 scenario (Figure 4.5). Temperature stabilization is achieved by switching off all the anthropogenic emissions around the time that GSAT first reaches the stabilization thresholds. Simulations have been observationally constrained to correct for a model bias in simulated historical September sea ice extent. In these simulations, Arctic sea ice coverage in September is simulated, on average, to drop below 1 million km² around 2040, consistent with the AR5 set of assessed models (Sigmond et al., 2018). The individual model simulations, for which there are twenty for each stabilized temperature level, show that the probability of the Arctic becoming practically ice free at the end of the 21st century is significantly higher for 2°C warming than for 1.5°C warming above 1850–1900 levels (high confidence).

The AR5 concluded with very high confidence that ocean carbon uptake of anthropogenic CO₂ will continue under all RCPs through the 21st century, with higher uptake corresponding to higher concentration pathways. The future evolution of the land carbon uptake was assessed to be much more uncertain than for ocean carbon uptake, with a majority of CMIP5 models projecting a continued cumulative carbon uptake.

We first summarize the assessment of past NAM changes and their attribution from Chapters 2 and 3 to put into context the future projections described here. Strong positive trends for the NAM/NAO indices were observed since 1960, which have weakened since the 1990s (high confidence) (Section 2.4.1.1). The NAO variability in the instrumental record was likely not unusual in the millennial and multi-centennial context (Section 2.4.1.1). Climate models simulate the gross features of the NAM with reasonable fidelity, including its interannual variability, but models tend to systematically underestimate the amount of multi-decadal variability of the NAM and jet stream compared to observations (Section 3.7.1; J. Wang et al., 2017b; Bracegirdle et al., 2018; Simpson et al., 2018), with the caveat of the observational record being relatively short to characterize decadal variability (Chiodo et al., 2019). A realistic simulation of the stratosphere and SST variability in the tropics and northern extratropics are important for a model to realistically capture the observed NAM variability. Despite some evidence from climate model studies that anthropogenic forcings influence the NAM, there is limited evidence for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM over the instrumental period (Section 3.7.1).

Significant progress has been made since AR5 in understanding the physical mechanisms responsible for changes in the NAM, although uncertainties remain. It is now clear from the literature that the NAM response, and the closely-related response of the mid-latitude storm tracks, to anthropogenic forcing in CMIP5-era climate models is determined by a ‘tug-of-war’ between two opposing processes (Harvey et al., 2014; Shaw et al., 2016; Screen et al., 2018a): (i) Arctic amplification (Sections 4.5.1.1 and 7.4.4.1), which decreases the low-level meridional temperature gradient, reduces baroclinicity on the poleward flank of the eddy-driven jet, and shifts the storm tracks equatorward and leading to a negative NAM (see Box 10.1; Harvey et al., 2015; Hoskins and Woollings, 2015; Peings et al., 2017; Screen et al., 2018a); and (ii) enhanced warming in the tropical upper-troposphere, due to GHG increases and associated water vapour and lapse rate feedbacks, which increases the upper-level meridional temperature gradient and causes a poleward shift of the storm tracks and a positive NAM (Harvey et al., 2014; Vallis et al., 2015; Shaw, 2019). The large diversity in projected NAM changes in CMIP5 multi-model ensemble (Gillett and Fyfe, 2013) appears to be at least partly explained by the relative importance of these two mechanisms in particular models (Harvey et al., 2014, 2015; Vallis et al., 2015; McCusker et al., 2017; Oudar et al., 2017). Models that produce larger Arctic amplification also tend to produce larger equatorward shifts of the mid-latitude jets and associated negative NAM responses (Barnes and Polvani, 2015; Harvey et al., 2015; Zappa and Shepherd, 2017; McKenna et al., 2018; Screen et al., 2018a; Zappa et al., 2018).

Another area of progress is new understanding the role of cloud radiative effects in shaping the mid-latitude circulation response to anthropogenic forcing. Through their non-uniform distribution of radiative heating, cloud changes can modify meridional temperature gradients and alter mid-latitude circulation and the annular modes in both hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects of changing upper and lower tropospheric temperature gradients on the NAM, progress has been made since AR5 in understanding the effect of simulated changes in the strength of the stratospheric polar vortex on winter NAM projections (Manzini et al., 2014; Zappa and Shepherd, 2017; Simpson et al., 2018).

The ‘wet get wetter, dry get drier’ paradigm, which has been used to explain the global precipitation pattern responding to global warming (Held and Soden, 2006), might not hold, especially over subtropical land regions (Greve et al., 2014; Feng and Zhang, 2015; Greve and Seneviratne, 2015). Over the tropical oceans, precipitation changes are largely driven by the pattern of SST changes (He et al., 2018), and in the subtropics, precipitation response is driven primarily by the fast adjustment to CO₂ forcing (He and Soden, 2017). In addition to the response to GHG forcing, forcing from natural and anthropogenic aerosols exert impacts on regional patterns of precipitation (Section 10.3.1; Ramanathan et al., 2005; Bollasina et al., 2011; Polson et al., 2014; Krishnan et al., 2016; L. Liu et al., 2018; Shawki et al., 2018). The large uncertainties in near-term regional precipitation projections arise due to the interplay between internal variability and anthropogenic external forcing (Endo et al., 2018; Wang et al.,2021). Uncertainties in future aerosol emissions scenarios contribute to uncertainties in regional precipitation projections (Wilcox et al., 2020). Aerosol changes induce a drying in the SH tropical band compensated by wetter conditions in the NH counterpart (Acosta Navarro et al., 2017). The spatially uneven distribution of the aerosol forcing may also induce changes in tropical precipitation caused by shifts in the mean location of the intertropical convergence zone (ITCZ) (Hwang et al., 2013; Ridley et al., 2015; Voigt et al., 2017). Because of the large uncertainty in the aerosol radiative forcing and the dynamical response to the aerosol forcing there is medium confidence in the impacts of aerosols on near-term projected changes in precipitation. Precipitation changes in the near term show seasonal amplification, precipitation increase in the rainy season and decrease in the dry season (Fujita et al., 2019).

Consistent with AR5, we conclude that projected changes of seasonal mean precipitation in the near term will increase at high latitudes. Near-term projected changes in precipitation are uncertain mainly because of natural internal variability, model uncertainty, and uncertainty in natural and anthropogenic aerosol forcing (medium confidence).

The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September will drop below 1 million km², or become practically ice-free, at some point between 2040 and 2060 (Collins et al., 2013). Since AR5, there has been substantial progress in understanding the response of Arctic sea ice to near-term changes in external forcing. In particular, it is very likely that different trajectories of the near-term evolution of anthropogenic forcing cause distinctly different likelihood ranges for very low sea ice coverage to occur over the next two decades (Notz and Stroeve, 2018). For example, there is an unlikely drop of September Arctic sea ice coverage to below 1 million km² before 2040 for RCP 2.6, and a likely drop of September Arctic sea ice coverage to below 1 million km² before 2040 for RCP 8.5 (medium confidence given the single study). The much higher likelihood of a practically sea ice free Arctic Ocean during summer before 2040 in RCP8.5 compared to RCP2.6 is consistent with related studies assessed in SROCC that find a substantially increased likelihood of an ice-free Arctic Ocean for 2.0°C compared to 1.5°C mean global warming relative to pre-industrial levels (Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond et al., 2018; Olson et al., 2019).

On seasonal to interannual time scales, there is new evidence since AR5 that initialized predictions show lower potential predictability for the boreal winter NAO than the correlation skill with respect to observations (Eade et al., 2014; Baker et al., 2018; Scaife and Smith, 2018; Athanasiadis et al., 2020). This has been referred to in the literature as a ‘signal-to-noise paradox’ and means that very large ensembles of predictions are needed to isolate the predictable component of the NAO. While the processes that contribute to the predictability of the winter NAO on seasonal time scales may be distinct from the processes that drive multi-decadal trends, there is emerging evidence that initialized predictions also underrepresent the predictability of the winter NAO on decadal time scales (D.M. Smith et al., 2019). Post-processing and aggregation of initialized predictions may therefore reveal significant skill for predicting the winter NAO on decadal time scales (Smith et al., 2020). Considering these new results since AR5, in the near-term it is likely that any anthropogenic forced signal in the NAM will be of comparable magnitude or smaller than natural internal variability in the NAM (medium confidence).

An influence of forcing agents other than stratospheric ozone and GHGs, such as anthropogenic aerosols, on SAM changes over the historical period has been reported in some climate models (Rotstayn, 2013), but the response across a larger set of CMIP5 models is not robust (Steptoe et al., 2016) and depends on how tropospheric temperature responds to aerosols (Choi et al., 2019). This gives low confidence in the potential influence of anthropogenic aerosols on the SAM in the future.

The AR5 assessed that it is very likely that the ENSO will remain the dominant mode of interannual variability in the future but did not specify its change in near term. A subset of CMIP5 models that simulate the ENSO Bjerknes index most realistically show an increase of ENSO SST amplitude in the near-term future and decline thereafter (Kim et al., 2014). However, detection of robust near-term changes of ENSO SST variability in response to anthropogenic forcing is difficult to achieve due to pronounced unforced low-frequency modulations of ENSO (Wittenberg, 2009; Maher et al., 2018; Wengel et al., 2018). Figure 4.10 in Section 4.3.3.2, using CMIP6 models, also shows no robust change in ENSO SST variability in the near term.

The Atlantic Multi-decadal Variability (AMV) is a large-scale climate mode accounting for the main fluctuations in North Atlantic SST on multi-decadal time scales (Section AIV.2.7). The AMV influences air temperatures and precipitation over adjacent and remote continents, and its undulations can partially explain the observed variations in the NH mean temperatures (Steinman et al., 2015). The origin of this variability is still uncertain. Ocean dynamics (e.g., changes in the AMOC), external forcing, and local atmospheric forcing all seem to play a role (Menary et al., 2015; Ruprich-Robert and Cassou, 2015; Brown et al., 2016; Cassou et al., 2018; Wills et al., 2019). Recent studies have discussed that the ocean dynamics play an active role in generating AMV (Oelsmann et al., 2020) and its interplay with the NAO (Vecchi et al., 2017; R. Zhang et al., 2019; Kim et al., 2020), although natural and anthropogenic external forcing might be crucial in modulating its amplitude and timing (Bellucci et al., 2017; Bellomo et al., 2018; Andrews et al., 2020; Borchertet al., 2021; Mann et al., 2021; see Sections 3.7.7 and AIV.2.7).

Mitigation of SLCFs affects future climate projections and could alter the time of emergence of anthropogenic climate change signals. The AR5 assessed that emission reductions aimed at decreasing local air pollution could have a near-term warming impact on climate (high confidence) (Kirtman et al., 2013). Because of their shorter lifetimes, reductions in emissions of SLCF species mainly influence near-term GSAT trends (Chalmers et al., 2012; Shindell et al., 2017; Shindell and Smith, 2019), but on decadal time scales the near-term response to even very large reductions in SLCFs may be difficult to detect in the presence of large internal climate variability (Samset et al., 2020). The changes in SLCF emissions during the COVID-19 pandemic has resulted in a small net radiative forcing without a discernible impact on GSAT (Cross-Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in the mid- to long-term (Smith and Mizrahi, 2013; Stohl et al., 2015; Hienola et al., 2018) and can influence peak warming during the 21st century (Rogelj et al., 2014; Hienola et al., 2018). This section focuses on the total effect of SLCF changes on GSAT projections in the SSP scenarios. A more detailed breakdown of the separate climate effects of SLCF species and precursor species can be found in Sections 6.7.2 and 6.7.3.

The main uncertainties in climate effects of SLCFs in the future come from: (i) the uncertainty in anthropogenic aerosol ERF (Section 7.3.3); (ii) uncertainty in the relative emissions of different SLCFs that have warming and cooling effects in the current climate (Section 6.2); and (iii) physical uncertainty including the efficacy of the climate response to SLCFs compared to long-lived GHGs (Marvel et al., 2016; Richardson et al., 2019). One example of physical uncertainty is that the shortwave radiative forcing from methane was neglected in previous calculations (Etminan et al., 2016; Collins et al., 2018), which affects understanding of present day and future methane ERF (Modak et al., 2018). Another example of physical uncertainty is projected changes in lightning-NO_x production, which contribute to future ozone radiative forcing (Banerjee et al., 2014, 2018; Finney et al., 2018).

In AR5, uncertainty due to future volcanic activity was not considered in the assessment of the CMIP5 21st century climate projections (Taylor et al., 2012; O’Neill et al., 2016). Since AR5, there has been considerable progress in quantifying the impacts of volcanic eruptions on decadal climate prediction and longer-term climate projections (Meehl et al., 2015; Swingedouw et al., 2015, 2017; Timmreck et al., 2016; Bethke et al., 2017; Illing et al., 2018). By exploring 60 possible volcanic futures under RCP4.5, it has been demonstrated that the inclusion of time-varying volcanic forcing may enhance climate variability on annual-to-decadal time scales (Bethke et al., 2017). Consistent with a tropospheric cooling response, the change in ensemble spread in the volcanic cases is skewed towards lower GSAT relative to the non-volcanic cases (Cross-Chapter Box 4.1, Figure 1). In these simulations with multiple volcanic forcing futures there is: (i) an increase in the frequency of extremely cold individual years; (ii) an increased likelihood of decades with negative GSAT trend (decades with negative GSAT trends become 50% more commonplace); (iii) later anthropogenic signal emergence (the mean time at which the signal of global warming emerges from the noise of natural climate variability is delayed almost everywhere) (high confidence); and (iv) a 10% overall reduction in global land monsoon precipitation and a 20% overall increase in the ensemble spread (Man et al., 2021).

Most of the projected changes in precipitation exhibit a sharp contrast between land and ocean (Sections 8.2.1 and 8.4.1). Temperature-driven intensification of land-mean precipitation during the 20th century has been masked by fast precipitation responses to anthropogenic sulphate and volcanic forcing (Allen and Ingram, 2002; Richardson et al., 2018a). Based on the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), land-mean precipitation is expected to increase more rapidly with the projected decrease in sulphate forcing and continued warming, contributing to increased global mean precipitation (Table 4.3) and will be clearly observable by the mid-21st century based on RCP4.5 and RCP8.5 scenarios (Richardson et al., 2018a).

The anthropogenic forced signal in extratropical atmospheric circulation may well be small compared to internal variability (Deser et al., 2012b, 2014) and, as assessed in AR5, there is generallylow agreement across models in many aspects of regional atmospheric circulation change particularly in the NH (Shepherd, 2014). The latter means that, in some regions, a multi-model average perspective of atmospheric circulation change represents a small residual after averaging over large intermodel spread. This is in strong contrast to thermodynamic aspects of climate change, such as surface temperature change, for which model results are generally highly consistent (see, e.g., Figure 4.19). Furthermore, models share systematic biases in some aspects of extratropical atmospheric circulation such as mid-latitude jets, which can have complex implications for understanding forced changes (Simpson and Polvani, 2016). Given these issues, an emerging field of research since AR5 has focused on the development of ‘storylines’ for regional atmospheric circulation change (Shepherd, 2019). The storyline approach is grounded in the identification of a set of physical predictors of atmospheric circulation change, such as those described above (Harvey et al., 2014; Manzini et al., 2014; Shepherd et al., 2018), which act together to determine a specific outcome in the projected atmospheric circulation change. The consequences of multi-model spread in the physical predictors of atmospheric circulation change can be investigated, conditioned on a specified level of global warming (see also Section 1.4.4.2 and Box 10.2; Zappa and Shepherd, 2017; Zappa, 2019; Mindlin et al., 2020).

The model-simulated, long-term trend of ocean acidification is assessed in Section 4.3.2.5 and Chapter 5 (Section 5.3.4.1). It is virtually certain that surface ocean acidification will continue in response to the rise in atmospheric CO₂, and continued penetration of anthropogenic CO₂ from the surface to the deep ocean will acidify the ocean interior (Figure 4.29). By the end of this century, under SSP3-7.0, a pH reduction of about 0.3 is found at a few hundred metres depth of the global ocean, with stronger acidification in the interior North Atlantic and the mid- to high-latitude Southern Ocean. At a depth of about 1 km, a pH reduction of about 0.1 is found.

Projections with CMIP6 ESMs (Kwiatkowski et al., 2020) show a surface pH decline of –0.16 ± 0.002 (±1 standard deviation) under SSP1-2.6 and –0.44 ± 0.005 under SSP5-8.5 from 1870–1899 to 2080–2099. The high-latitude oceans, in particular the Arctic, show greater decline in pH and accelerated acidification (Terhaar et al., 2020). For the same period, model-projected bottom-water pH decline is –0.018 ± 0.001 under SSP1-2.6 and –0.030 ± 0.002 under SSP5-8.5. The projected large-scale surface ocean acidification will be primarily determined by the pathway of atmospheric CO₂, with weak dependence on change in climate (high confidence) (Section 5.3.4.1; Hurd et al., 2018). However, for a given atmospheric CO₂ scenario, uncertainty in projected ocean acidification increases with ocean depth because of model-simulated differences in ocean circulation that transports anthropogenic CO₂ from the surface to bottom ocean (high confidence) (Kwiatkowski et al., 2020). For example, projected surface pH fully separates between SSPs scenarios before 2050, but some overlap across SSPs is still found for projected bottom-water pH in 2080 (Kwiatkowski et al., 2020).

There is no consensus on changes in amplitude of ENSO SST variability across CMIP iterations. The main factors driving the diversity of ENSO SST amplitude change in climate models are internal variability, SST-mean warming pattern, and model systematic biases. First, pronounced low-frequency modulations of ENSO exist even in unforced control simulations due to internal variability, which leads a large uncertainty in quantifying future ENSO changes (Wittenberg, 2009; Vega-Westhoff and Sriver, 2017; Zheng et al., 2018). Second, ENSO characteristics depend on the climate mean state of the tropical Pacific; however, ENSO can also influence the mean state through non-linear processes (Cai et al., 2015; Timmermann et al., 2018). The response of the tropical Pacific mean state to anthropogenic forcing is characterized by a faster warming on the equator compared to the off-equatorial region, a faster warming of the eastern equatorial Pacific compared to the central tropical Pacific (e.g., El Niño-like mean SST warming, see Section 7.4.4.2), and a weakening of the Walker circulation in most models. Those models with a El Niño-like warming tend to project a strengthening of ENSO SST variability whereas models with a La Niña-like warming tend to project a weakening of variability (Zheng et al., 2016; Kohyama and Hartmann, 2017; J. Wang et al., 2017b; Cai et al., 2018a; Fredriksen et al., 2020). Third, how to take model biases into account leads to different ENSO changes. Kim et al. (2014) suggested that a subset of CMIP5 models that simulate linear ENSO stability realistically exhibit a decrease in ENSO amplitude by the second half of the 21st century. However, an increase of ENSO SST variability has been projected when considering biases in ENSO pattern simulation by different models (Zheng et al., 2016; Cai et al., 2018a). This highlights the importance of constraining tropical Pacific mean state changes in order to enhance confidence in the projected response of ENSO.

Global warming of 1.5°C implies higher mean temperatures compared to 1850–1900, with generally higher warming over land compared to ocean areas (virtuallycertain) and larger warming in high latitudes compared to low latitudes (Figure 4.31). In addition, global warming of 2°C versus 1.5°C results in robust increases in the mean temperatures in almost all locations, both on land and in the ocean (virtually certain), with subsequent further warming at almost all locations at higher levels of global warming (virtually certain) (Hoegh-Guldberg et al., 2018). For each particular level of global warming, relatively larger mean warming is projected for land regions (virtually certain) (see Figure 4.31; Christensen et al., 2013; Collins et al., 2013; Seneviratne et al., 2016). The projected changes at 1.5°C and 2°C global warming are consistent with observed historical global trends in temperature and their attribution to anthropogenic forcing (Chapter 3), as well as with observed changes under the recent global warming of 0.5°C (Schleussner et al., 2017; Hoegh-Guldberg et al., 2018). That is, spatial patterns of temperature changes associated with the 0.5°C difference in GMST warming between 1991–2010 and 1960–1979 (Schleussner et al., 2017; Hoegh-Guldberg et al., 2018) are consistent with projected changes under 1.5°C and 2°C of global warming.

Most strong-mitigation scenarios assume – in addition to emissions reductions – some form of carbon dioxide removal (CDR). Anthropogenic activities that remove CO₂ from the atmosphere and durably store it in geological, terrestrial, or ocean reservoirs, or in products (see Glossary). The SR1.5 (Rogelj et al., 2018b) assessed that all pathways that limit warming to 1.5°C by 2100 with no or limited overshoot use CDR. In the SSP class of scenarios, SSP1-1.9 is characterized by a rapid decline of net CO₂ emissions to zero by 2050 and net negative CO₂ emissions in the second half of this century (O’Neill et al., 2016; Rogelj et al., 2018a), implying the use of CDR. The term ‘net CO₂ emissions’ refers to the difference between anthropogenic CO₂ emissions and removal by CDR options, and ‘net negative CO₂ emissions’ imply a scenario where CO₂ removal exceeds emissions (van Vuuren et al., 2011, 2016). The terms ‘negative emissions’ and ‘net negative emissions’ refer to and include all GHGs (see Glossary).

Deployment of CDR will lead to a reduction in atmospheric CO₂ levels only if uptake by sinks exceeds net CO₂ emissions. Hence, there could be a substantial delay between the initiation of CDR and net CO₂ emissions turning negative (van Vuuren et al., 2016), and the time to reach net negative CO₂ emissions and the evolution of atmospheric CO₂ and climate thereafter would depend on the combined pathways of anthropogenic CO₂ emissions, CDR, and natural sinks. The cooling (or avoided warming) due to CDR would be proportional to the cumulative amount of CO₂ removed from the atmosphere by CDR (Tokarska and Zickfeld, 2015; Zickfeld et al., 2016), as implied by the near-linear relationship between cumulative carbon emissions and GSAT change (Section 5.5).

Emissions pathways that limit globally averaged warming to 1.5°C or 2°C by the year 2100 assume the use of CDR approaches in combination with emissions reductions to follow net negative CO₂ emissions trajectory in the second half of this century. For instance, in SR1.5, all analysed pathways limiting warming to 1.5°C by 2100 with no or limited overshoot include the use of CDR to some extent to offset anthropogenic CO₂ emissions and the median of CO₂ removal across all scenarios was 730 GtCO₂ in the 21st century (Rickels et al., 2018; Rogelj et al., 2018b). Affordable and environmentally and socially acceptable CDR options at scale well before 2050 are an important element of 1.5°C-consistent pathways especially in overshoot scenarios (de Coninck et al., 2018). The required scale of removal by CDR can vary from 1–2 GtCO₂ yr^–1year from 2050 onwards to as much as 20 GtCO₂ yr^–1 (Waisman et al., 2019). In the SSP class of scenarios, net CO₂ emissions turn negative from around 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the overshoot scenario SSP5-3.4-OS (O’Neill et al., 2016). Thus, CDR would play a pivotal role in limiting climate warming to 1.5°C or 2°C (Minx et al., 2018). In stark contrast, however, two extensive reviews (Lawrence et al., 2018; Nemet et al., 2018) conclude that it is implausible that any CDR technique can be implemented at the scale needed by 2050.

For the same amount of global mean cooling achieved, the pattern of climate response would depend on SRM characteristics (Niemeier et al., 2013; Duan et al., 2018; Muri et al., 2018). This is illustrated in Figure 4.38 for temperature and precipitation change relative to a high-CO₂ world for scenarios of CO₂ reduction, solar irradiance reduction, SAI, and MCB. The pattern differences for different methods are much larger for precipitation than for temperature. The pattern of climate change resulting from SRM is also different from that resulting from CO₂ reduction (Figure 4.38). It is virtually certain that SRM approaches would not be able to precisely offset the GHG-induced anthropogenic climate change at global and regional scales.

There is large uncertainty in the stratospheric response to SAI, and the change in stratospheric dynamics and chemistry would depend on the amount, size, type, location, and timing of injection. There is high confidence that aerosol-induced stratospheric heating will play an important role in surface climate change (Simpson et al., 2019b) by altering the effective radiative forcing (Krishnamohan et al., 2019), lower stratosphere stability (Ferraro and Griffiths, 2016), quasi-biennial oscillation (QBO) (Aquila et al., 2014; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018), polar vortexes (Visioni et al., 2020a), and North Atlantic Oscillation (Jones et al., 2021). Model simulations indicate stronger polar jets and weaker storm tracks and a poleward shift of the tropospheric mid-latitude jets in response to stratospheric sulphate injections in the tropics (Ferraro et al., 2015; Richter Jadwiga et al., 2018), as the meridional temperature gradient is increased in the lower stratosphere by the aerosol-induced heating. The aerosol-induced warming would also offset some of the GHG-induced stratospheric cooling. Compared to equatorial injection, off-equatorial injection is likely to result in reduced change in stratospheric heating, circulation, and QBO (Richter Jadwiga et al., 2018; Kravitz et al., 2019). Stratospheric ozone response to sulphate injection is uncertain depending on the amount, altitude, and location of injection (WMO, 2018). It is likely that sulphate injection would cause a reduction in polar column ozone concentration and delay the recovery of Antarctic ozone hole (Pitari et al., 2014; Richter Jadwiga et al., 2018; Tilmes et al., 2018b), which would have implications for UV radiation and surface ozone (Pitari et al., 2014; Xia et al., 2017; Richter Jadwiga et al., 2018; Tilmes et al., 2018b). Injection of non-sulphate aerosols is likely to result in less stratospheric heating and ozone loss (Pope et al., 2012; Weisenstein et al., 2015; Keith et al., 2016). One side effect of SAI is increased sulphate deposition at surface. A recent modelling study indicates that to maintain global temperature at 2020 levels under RCP 8.5, increased sulphate deposition from stratospheric sulphate injection could be globally balanced by the projected decrease in tropospheric anthropogenic SO₂ emissions, but the spatial distribution of sulphate deposition would move from low to high latitudes (Visioni et al., 2020c).

Marine cloud brightening (MCB) involves injecting small aerosols such as sea salt into the base of marine stratocumulus clouds where the aerosols act as cloud condensation nuclei (CCN). In the absence of other changes, an increase in CCN would produce higher cloud droplet number concentration with reduced droplet sizes, increasing cloud albedo. Increased droplet concentration may also increase cloud water content and optical thickness, but recent studies suggest that liquid water path response to anthropogenic aerosols is weak due to the competing effects of suppressed precipitation and enhanced cloud water evaporation (Toll et al., 2019). An analogue for MCB are reflective, persistent ‘ship tracks’ observed after the passage of a sea-going vessel emitting combustion aerosols into susceptible clouds (Christensen and Stephens, 2011; Chen et al., 2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020) found a substantial increase in cloud reflectivity from shipping in south-east Atlantic basin, suggesting that a regional-scale test of MCB in stratocumulus‐dominated regions could be successful.

The zero emissions commitment (ZEC) is the climate change commitment that would result, in terms of projected GSAT, from setting carbon dioxide (CO₂) emissions to zero. It is determined by both inertia in physical climate system components (ocean, cryosphere, land surface) and carbon cycle inertia (see Annex VII). In its widest sense it refers to emissions of all compounds including greenhouses gases, aerosols and their pre-cursors. A specific sub-category of zero emissions commitment is the zero CO₂ emissions commitment, which refers to the climate system response to a cessation of anthropogenic CO₂ emissions excluding the impact of non-CO₂ forcers. Assessment of remaining carbon budgets requires an assessment of zero CO₂ emissions commitment as well as of the transient climate response to cumulative carbon emissions (TCRE; Section 5.5.2).

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Regional climate change refers to a change in climate in a given region (Section 10.1.2.1) identified by changes in the mean or higher moments of the probability distribution of a climate variable and persisting for a few decades or longer. It can also refer to a change in temporal properties such as persistence and frequency of occurrence of weather and climate extreme events. Regional climate change may be caused by natural internal processes such as atmospheric internal variability and local climate response to low-frequency modes of climate variability (Technical Annex IV), as well as by changes in external forcings such as modulations of the solar cycle, orbital forcing, volcanic eruptions, and persistent anthropogenic changes in the composition of the atmosphere or in land use and land cover (Cross-Chapter Box 3.2; IPCC, 2018a), in addition to the interactions and feedbacks between them. Process interaction in space is pervasive, which means that small spatial scales often have an influence on the larger scales (Palmer, 2013; Sandu et al., 2016). Depending on the context, a region may refer to a large area such as a monsoon region, but may also be confined to smaller areas such as coastlines, mountain ranges or human settlements like cities. Users (understood as anyone incorporating climate information into their activity) often request climate information for these range of scales since their operating and adaptation decision scales range from the local to the sub-continental level.

Given the many types of regional climates, the broad range of spatial and temporal scales (Section 10.1.2), and the diversity of user needs, a variety of methodologies and approaches have been developed to construct regional climate change information. The sources include global and regional climate model simulations, statistical downscaling and bias adjustment methods. A commonly used source is long-term (end-of-century) model projections of regional climate change, as well as near-term (next 10 years) climate predictions (Kushnir et al., 2019; Rössler et al., 2019a). Regional observations, with their associated challenges, are a key source for the regional climate information construction process (Q. Li et al., 2020). High-quality observations that enable monitoring of the regional aspects of climate are used to adjust inherent model biases and are the basis for assessing model performance. Process understanding and attribution of observed changes to large- and regional-scale anthropogenic and natural drivers and forcings are also important sources.

The chapter (Figure 10.2) starts with an introduction of the concepts used in the distillation of regional climate information (Section 10.1). Section 10.2 addresses the aspects associated with the access to and use of observations, while different modelling approaches are introduced and assessed in Section 10.3. Section 10.3 also addresses the performance of models in simulating relevant climate characteristics as needed to estimate the credibility of future projections. Section 10.4 assesses the interplay between anthropogenic causes and internal variability at regional scales, and its relevance for the attribution of regional climate changes and the emergence of regional climate change signals. Section 10.5 tackles the issue of how regional climate information is distilled from different sources taking into account the context and the values of both the producer and the user. Section 10.6 illustrates the distillation approach using three comprehensive examples. Finally, Section 10.7 lists some limitations to the assessment of regional climate information.

Variability in regional climate arises from natural and anthropogenic forcings, internal variability including the local expression of large-scale remote drivers (also known as teleconnections), and the feedbacks between them. Due to the many possible drivers of variability and change (Figure 10.3), quantifying the interplay between internal modes of decadal variability and any externally forced component is crucial in attempts to attribute causes of regional climate changes (e.g., Hoell et al., 2017; Nath et al., 2018). A regional climate signal could arise purely due to some anthropogenic influence or conversely, entirely due to internal variability, but it is most likely the result of a combination of both (Section 10.4). This section briefly introduces these sources of regional variability and should be read along with corresponding sections in Chapters 3, 6 and 7. Section 10.3 assesses their representation in climate models, Section 10.4 discusses their relevance for the attribution of multi-decadal trends and Section 10.6 refers to them as sources in specific examples where regional climate information is built. Section 8.2 offers a companion discussion focussing on changes in the water cycle. An example of how changes in one region could act as a source for changes in a neighbouring one is assessed in the Cross-Chapter Box 10.1 for the linkages between polar and mid-latitude regions, an interaction that has led to substantial recent research. This section also introduces the sources of uncertainty in model-derived regional climate information and how the quantification of the uncertainties influences the confidence of the regional climate information.

Both natural and anthropogenic aerosols are often emitted at a regional scale, have a short atmospheric lifetime (from a few hours to several days; Section 6.1), are dispersed regionally and affect climate at a regional scale through radiative cooling/heating and cloud microphysical effects (Chapter 8; Rotstayn et al., 2015; Sherwood et al., 2015). The majority of aerosols scatter solar radiation, but with strong regional variations (Shindell and Faluvegi, 2009) that lead to regional radiative effects of up to two orders of magnitude larger than the global average (B. Li et al., 2016; K. Li et al., 2016; Mallet et al., 2016). Black carbon, instead, is known to absorb solar radiation, leading to regional atmospheric warming patterns due to its inhomogeneous spatial distribution (Gustafsson and Ramanathan, 2016). Patterns of forcing generally follow those of aerosol burden. However, temperature and precipitation responses are both local and remote (Z. Li et al., 2016; Kasoar et al., 2018; L. Liu et al., 2018; Samset et al., 2018; Thornhill et al., 2018; Westervelt et al., 2018). For instance, changes in aerosol concentrations in the NH have been reported to modulate monsoon precipitation in West Africa and the Sahel (Undorf et al., 2018; Section 10.4.2.1) and in Asia (H. Zhang et al., 2018; Section 10.6.3).

Anthropogenic aerosols play a key role in climate change (Chapter 6). Although the global mean optical depth caused by anthropogenic aerosols did not change from 1975 to 2005 (Chapter 6), the regional pattern changed dramatically between Europe and eastern Asia (Fiedler et al., 2017, 2019; Stevens et al., 2017). Large regional differences in present-day aerosol forcing exist with consequences for regional temperature, hydrological cycle and modes of variability (Chapter 8, Section 10.6). Examples of regions with a notable role for anthropogenic aerosol forcing are the Indian monsoon region (Section 10.6.3) and the Mediterranean basin Section 10.6.4). Anthropogenic aerosols are also very relevant in many urban areas (Box 10.3; Gao et al., 2016; Kajino et al., 2017).

Atmospheric modes of variability may have seasonally-dependent regional effects like the North Atlantic Oscillation (NAO) in European winter (Tsanis and Tapoglou, 2019) and summer (Bladé et al., 2012; Dong et al., 2013). Even though these modes are internal to the climate system, their variability can be affected by anthropogenic forcings. For instance, the SAM (Hendon et al., 2014) is both internally driven (Smith and Polvani, 2017), but also affected by recent stratospheric ozone changes (Bandoro et al., 2014). The teleconnections between these modes of variability and surface weather often exhibit considerable non-stationarity (Hertig et al., 2015).

Due to the large ocean heat capacity and their long temporal scales, multi-annual to multi-decadal modes of ocean variability such as the Pacific Decadal Variability (PDV; Newman et al., 2016) and the Atlantic Multi-decadal Variability (AMV; Buckley and Marshall, 2016) are key drivers of regional climate change. In the case of the AMV both natural (volcanic) and anthropogenic (aerosol) external forcings are thought to be involved in its timing and intensity (Section 3.7.7). These modes not only affect nearby regions but also remote parts of the globe through atmospheric teleconnections (Meehl et al., 2013; Dong and Dai, 2015) and can act to modulate the influence of natural and anthropogenic forcings (Davini et al., 2015; Ghosh et al., 2017; Ménégoz et al., 2018b). The dynamics of the ocean modes is simultaneously affected by other modes of variability spanning the full range of spatial and temporal scales due to non-linear interactions (Figure 10.3; Kucharski et al., 2010; Dong et al., 2018). This mutual interdependence can result in changing characteristics of the connection over time (Gallant et al., 2013; Brands, 2017; Dong and McPhaden, 2017), and of their regional climate impact (Martín-Gómez and Barreiro, 2016, 2017). As with atmospheric modes of variability, the regional influence of ocean modes of variability on regional climates can be seasonally dependent (Haarsma et al., 2015).

The SROCC (IPCC, 2019b) stated that observations and models for assessing changes in the ocean and the cryosphere have been developed considerably during the past century but observations in some key regions remain under-sampled and were very short relative to the time scales of natural variability and anthropogenic changes. Retreat of mountain glaciers and thawing of mountain permafrost continues and will continue due to significant warming in those regions, where it is likely to exceed global temperature increase.

The SROCC assessed that it is virtually certain that Antarctica and Greenland have lost mass over the past decade and observed glacier mass loss over the last decades is attributable to anthropogenic climate change (high confidence). It is virtually certain that projected warming will result in continued loss in Arctic sea ice in summer, but there is low confidence in climate model projections of Antarctic sea ice change because of model biases and disagreement with observed trends. Knowledge and observations of the polar regions were sparse compared to many other regions, due to remoteness and challenges of operating in them.

The Arctic has very likely warmed more than twice the global rate over the past 50 years with the greatest increase during the cold season (Atlas.11.2). Several mechanisms are responsible for the enhanced lower troposphere warming of the Arctic, including ice albedo, lapse rate, Planck and cloud feedbacks (Section 7.4.4.1). The rapid Arctic warming strongly affects the ocean, atmosphere, and cryosphere in that region (Section 2.3.2.1 and Atlas.11.2). Averaged over the decade 2010–2019, monthly average sea ice area in August, September and October has been about 25% smaller than during 1979–1988 (high confidence) (Section 9.3.1.1). It is very likely that anthropogenic forcings mainly due to greenhouse gas increases have contributed substantially to Arctic sea ice loss since 1979, explaining at least half of the observed long-term decrease in summer sea ice extent (Section 3.4.1.1).

Some recent regional climate changes can only be simulated by climate models if anthropogenic aerosols are correctly included (Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples of the importance of correctly representing anthropogenic aerosols are the recent enhanced warming over Europe (Nabat et al., 2014; Dong et al., 2017), the cooling over the East Asian monsoon region, leading to a weakening of the monsoon (Section 8.3.2.4; Song et al., 2014; Q. Wang et al., 2017), as well as changes in the monsoons of West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia (Sections 8.3.2.4 and 10.6.3; Undorf et al., 2018). The relevance of appropriately representing anthropogenic aerosols has been widely studied in regional models (Boé et al., 2020a; Gutiérrez et al., 2020), with an advantage for models with interactive aerosol schemes (Drugé et al., 2019; Nabat et al., 2020). Without a fully coupled chemistry module, radiative forcing can be simulated by including simple models of sulphate chemistry or specifying the optical properties from observations and prescribing the effect of aerosols on the cloud-droplet number (Fiedler et al., 2017, 2019; Stevens et al., 2017). In all cases, the specification of the aerosol load limits the trustworthiness of the simulations at the regional scale when enough detail is not provided (Samset et al., 2019; Shonk et al., 2020; Z. Wang et al., 2021).

RCM experiments are often set up such that changes in forcing agents are included only via the boundary conditions, but not explicitly included inside the domain. Jerez et al. (2018) demonstrated that not including time-varying GHG concentrations within the RCM domain may misrepresent temperature trends by 1–2°C per century. Including the past trend in anthropogenic sulphate aerosols in reanalysis-driven RCM simulations substantially improved the representation of recent brightening and warming trends in Europe (Nabat et al., 2014; see Sections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, Bukovsky (2012) argued that RCMs may not capture observed summer temperature trends in the USA because changes in land cover are not taken into account. Barlage et al. (2015) have revealed that including the behaviour of groundwater in land schemes increases the performance of an RCM model to represent climate variability in the central USA. Hamdi et al. (2014) found that an RCM that did not incorporate the historical urbanization in the land-use, land-cover scheme is not able to reproduce the warming trend observed in urban stations, with a larger bias for the minimum temperature trend.

Analysis of multi-model archives such as CMIP or CORDEX simulation results cannot easily disentangle model uncertainty and uncertainty related to internal variability. Since AR5, the development of single-model (global model and/or RCM) initial-condition large ensembles (SMILEs) has emerged as a promising way to robustly assess the regional-scale forced response to external forcings and the respective contribution of internal variability and model uncertainty to future regional climate changes (Section 4.2.5; Deser et al., 2014, 2020; Kay et al., 2015; Sigmond and Fyfe, 2016; Aalbers et al., 2018; Bengtsson and Hodges, 2019; Dai and Bloecker, 2019; Leduc et al., 2019; Maher et al., 2019; von Trentini et al., 2019; Lehner et al., 2020). The recent development of a multi-model archive of SMILE simulations facilitates the quantification and comparison of the influence of internal variability on global model-based regional climate projections between different models (Deser et al., 2020; Lehner et al., 2020). Another related development is the more frequent use of observation-based statistical models to assess the influence of internal variability on regional-scale global and regional model projections (Thompson et al., 2015; Salazar et al., 2016). However, these methods often implicitly assume that regional-scale internal variability does not change under anthropogenic forcing, which is a strong assumption that does not seem to hold at regional and local scales (LaJoie and DelSole, 2016; Pendergrass et al., 2017; W. Cai et al., 2018; Dai and Bloecker, 2019; Mankin et al., 2020; Milinski et al., 2020).

The use of SMILEs assumes that they have a realistic representation of internal variability and its evolution under anthropogenic climate change (Eade et al., 2014; McKinnon et al., 2017; McKinnon and Deser, 2018; Chen and Brissette, 2019). Assessing the realism of simulated internal variability for past and current climates remains an active research field with a number of issues such as the shortness and uncertainties of the observed record, in particular in data-scarce regions (Section 10.2.2.3), the signal-to-noise paradox (Section 4.4.3.1; Scaife and Smith, 2018), uncertainty in past observed external forcing estimates (Chapters 2, 6 and 7) and the limitations of assumptions underlying the statistical methods used to derive observational large ensembles (McKinnon et al., 2017; McKinnon and Deser, 2018; Castruccio et al., 2019). Calibration methods inspired by weather and seasonal forecasts can be used to improve the reliability of regional-scale climate projections from large ensembles (Brunner et al., 2019; O’Reilly et al., 2020). Interestingly, reliability is improved when the calibration is performed separately for the dynamical and residual components of the ensemble resulting from dynamical adjustment (Section 10.4.1; O’Reilly et al., 2020).

This section focuses on the assessment of the methodologies used to identify the physical causes of past and future regional climate change in the context of the ongoing anthropogenic influence on the global climate. The main foci are the attribution of past regional-scale changes (Sections 10.4.1–2) and the robustness and future emergence of the regional-scale response to anthropogenic forcing (Section 10.4.3).

In this chapter, regional-scale attribution is defined as the process of evaluating the relative contributions of multiple causal factors (or drivers) to regional climate change (Cross-Working Group Box: Attribution in Chapter 1; Rosenzweig and Neofotis, 2013; Shepherd, 2019). Attribution at regional scale builds upon the usual definition of attribution used in the AR5 (Cross-Working Group Box: Attribution in Chapter 1; Hegerl et al., 2010). However, in contrast with global-scale attribution methods where internal variability might be considered as a noise problem (Section 3.2), the preliminary detection step is not always required to perform regional-scale attribution since causal factors of regional climate change may also include internal modes of variability in addition to external natural and anthropogenic forcing. Importantly, regional-scale (or process-based) attribution also seeks to determine the physical processes and uncertainties involved in the causal factor’s influence (Cross-Working Group Box: Attribution in Chapter 1).

(Section 10.4.1 describes regional-scale attribution methodologies and assesses their application to regional changes of temperature and precipitation. Section 10.4.2 presents three illustrative attribution examples that illustrate a number of specific regional-scale challenges and methodological aspects. Section 10.4.3 focuses on methodologies used to assess the robustness and emergence of the regional climate response to anthropogenic forcing. A basic description of future regional climate change for all regions considered in the report (as defined in Section 1.4.5) appears in the Atlas.

Optimal fingerprint regression-based methods have been applied to detection and attribution of mean temperature anthropogenic signal in several regions of the world such as Canada, India, central Asia, northern and western China, Australia, and North Africa (Xu et al., 2015; C. Li et al., 2017; Dileepkumar et al., 2018; Y. Wang et al., 2018; Peng et al., 2019; Wan et al., 2019). The influence of anthropogenic forcing, and in particular that of greenhouse gases (GHGs), is robustly detected in annual and seasonal mean temperatures for all considered regions. Most of the observed regional temperature changes since the mid-twentieth century can only be explained by external forcings, with anthropogenic influence being the dominant factor. GHG increase is found to be the primary factor of the anthropogenic-induced warming while the aerosol forcing leads to a cooling offsetting a fraction of the GHG change (C. Li et al., 2016, 2017). While the influence of external natural forcing can often be detected as well, its contribution to observed changes is usually much smaller (C. Li et al., 2017; Wan et al., 2019). Temperature detection results are found to be robust to the use of different observational datasets and detection methodologies (Dileepkumar et al., 2018).

Detection of mean precipitation changes caused by human influence is much more difficult, due to a larger role of internal variability at regional to local scales, as well as substantial modelling and observational uncertainty (Wan et al., 2015; Sarojini et al., 2016; C. Li et al., 2017). However, multi-decadal precipitation changes due to anthropogenic forcing have been detected for several regions. Ma et al. (2017b) show that anthropogenic forcing has strongly contributed to the observed shift of China daily precipitation towards heavy precipitation. The observed weakening of the East Asia summer monsoon, also known as the southern flooding and northern drought pattern has been partially linked to anthropogenic forcing (Section 8.3.2.4.2; Song et al., 2014; Zhou et al., 2017; Tian et al., 2018). Changes in GHGs lead to increasing precipitation over southern China, while changes in anthropogenic aerosols over East Asia are the dominant factors determining drought conditions over northern China (Song et al., 2014; Tian et al., 2018). Based on all-forcing and single-forcing simulation ensembles with a high-resolution model, Delworth and Zeng (2014) found that the observed long-term regional austral autumn and winter rainfall decline over southern and particularly south-west Australia is partially reproduced in response to anthropogenic changes in GHGs and ozone in the atmosphere, whereas anthropogenic aerosols do not contribute to the simulated precipitation decline. In contrast, the observed increase of north-west Australian summer rainfall since 1950 has been partially attributed to anthropogenic aerosol based on CMIP5 detection and attribution single-forcing simulations (Section 8.3.2.4.6; Dey et al., 2019a, b).

Time scale separation methods such as the low-frequency component analysis and ensemble empirical mode decomposition methods take advantage of the longer time scale associated with anthropogenic external forcing compared to that of most internal modes of variability. The low-frequency component analysis method tries to find low-frequency variability patterns by searching for linear combinations of a moderate number of empirical orthogonal functions that maximize the ratio of low-frequency to total variance. It has first been used to separate internal modes of interannual and decadal variability from slowly varying and externally-forced variability in the Pacific and Atlantic oceans (Wills et al., 2018, 2019). The methodology has also been applied to patterns of observed surface air temperature to isolate the slow components of observed changes that are consistent with the expected response to anthropogenic greenhouse gas and aerosol forcing (Wills et al., 2020).

The ensemble empirical mode decomposition method (Wu and Huang, 2009; Wilcox et al., 2013; Ji et al., 2014; Qian and Zhou, 2014) decomposes data, such as time series of historical temperature and precipitation, into independent oscillatory modes of decreasing frequency. The last step of the method leaves behind a smooth and low-frequency residual time series. Typically, the non-linear anthropogenic trend (e.g., of 20th-century temperature) can be reconstructed by summing the long-term mean, the residual, and eventually the lowest-frequency mode to account for a multi-decadal forced signal, for instance associated with anthropogenic aerosol forcing. The ensemble empirical mode decomposition method is an example of a data-driven, non-parametric approach that can be used to directly provide an estimate of the forced response without the need for model data (Qian, 2016).

The univariate detection method does not use spatial pattern information, but compares observed trends in gridded datasets with distributions of trends from ensembles of simulations during the historical period (Knutson et al., 2013; Knutson and Zeng, 2018). The trends arising from simulations constrained by natural forcing-only and all-forcing are compared with distributions of trends purely due to internal variability and derived from long simulations with constant pre-industrial external forcing. Consistency between observed and simulated historical trends is also assessed with statistical tests that can be applied independently over a large number of grid points. The fraction of area over a given region where the change is classified as detectable, attributable, or consistent/inconsistent, is then finally estimated. The method can be viewed as a simple consistency test for both amplitude and pattern of observed versus simulated trends. Its application to CMIP3 and CMIP5 models suggests that 80% of the Earth’s surface has a detectable anthropogenic warming signal (Knutson et al., 2013). Regarding regional land precipitation changes over the 1901–2010 and 1951–2010 periods, application of the univariate detection method based on CMIP5 models suggests attributable anthropogenic changes at several locations such as increases over regions of the north-central USA, southern Canada, Europe, and southern South America and decreases over parts of the Mediterranean region, northern tropical Africa and south-western Australia (Delworth and Zeng, 2014; Knutson and Zeng, 2018).

The Sahel, fed by the West African monsoon, has experienced severe decadal rainfall variations (Figure 10.11a). Abundant rainfall in the 1950s–1960s was followed by a large negative trend (Figure 10.11b) until at least the 1980s, over which annual rainfall fell by 20–30% (Hulme, 2001). The subsequent partial recovery (B. Wang et al., 2021) is more uncertain: rain-gauge studies suggest a return to long-term positive anomalies in the western Sahel in the early 2000s (Panthou et al., 2018), while CHIRPS merged satellite/gauge data show a wetter western Sahel since 1981 (Bichet and Diedhiou, 2018a, b). The recovery has been more significant over the central rather than the western Sahel (Lebel and Ali, 2009; Maidment et al., 2015; Sanogo et al., 2015) and a multiple-gauge record supports a greater recovery to the eastern side (Nicholson et al., 2018). In this attribution example, drivers of the long-term drought and subsequent partial recovery are discussed, including anthropogenic GHG and aerosol emissions, and sea surface temperature (SST) variations that, in part, relate to internal variability. The reader is also referred to assessment in Section 8.3.2.4. We define the Sahel within 10°N–20°N across to 30°E, consistent with the eastern boundary used in Chapter 8, and the rainy season as spanning June to September.

In terms of anthropogenic emissions, regional aerosol emissions from Europe, and to a lesser extent from Asia, have been shown in a global model to weaken Sahel precipitation either through a weakened Saharan heat low or via the Walker circulation (Dong et al., 2014). Greenhouse gases (GHGs) and anthropogenic aerosol can be considered together to control ITCZ position based on temperature asymmetry at the hemispheric scale. GHGs increase Sahel precipitation, while aerosol reduces it (in coupled slab-ocean model experiments by Ackerley et al. (2011) following Biasutti and Giannini (2006)). This effect is stronger when models account for aerosol–cloud interactions (Allen et al., 2015). Perturbed physics GCM ensembles suggests that aerosol emissions were the main driver of observed drying over 1950–1980 (Ackerley et al., 2011), supported by CMIP5 single-forcing experiments (Polson et al., 2014). A coherent drying signal in CMIP5 over the extended 1901–2010 period has also been found, although smaller than the observed trend (Knutson and Zeng, 2018). By applying aerosol scaling factors to the historical period in order to sample the uncertainty in CMIP5 aerosol radiative forcing, Shonk et al. (2020) found differences of 0.5 mm day^–1 for Gulf of Guinea rainfall between strong and weak aerosol experiments as illustrated in Figure 10.11c, although the drying appears further south than observed due to model bias.

For the partial recovery in West African monsoon and Sahel rainfall since the late 1980s, a detection study using three reanalyses (Cook and Vizy, 2015) shows a connection to increasing Saharan temperatures at a rate two to four times greater than the tropical mean, also confirmed by multiple observational and satellite-based data (Zhou and Wang, 2016; Vizy and Cook, 2017) and the review of Cook and Vizy (2019). Reanalyses are also noted to significantly underestimate the Saharan warming (Zhou and Wang, 2016). Saharan warming causes a stronger thermal low and more intense monsoon flow, providing more moisture to the central and eastern Sahel, supported by CMIP5 models (Lavaysse et al., 2016), although not all models capture the observed rainfall–heat–low relationship. Sahel rainfall is also incorrectly located in prototype versions of a few CMIP6 models, related to tropospheric temperature biases (Martin et al., 2017). Amplified Saharan warming has increased the wind shear, leading to a tripling of extreme storms since 1982, which may partially explain the recovery (Taylor et al., 2017). Instead, observations, multiple models and SST-sensitivity experiments with AGCMs have suggested that stronger Mediterranean Sea evaporation enhances low-level moisture convergence to the Sahel, increasing rainfall (Park et al., 2016). Meanwhile, an AGCM study suggested that GHGs alone (in the absence of SST warming) could cause Sahel rainfall recovery, with an additional role for anthropogenic aerosol (Dong and Sutton, 2015); recent changes in North Atlantic SSTs, although substantial, did not exert a significant impact on the recovery. Large spread in the recovery in a five-member AGCM ensemble suggests that atmospheric internal variability cannot be discounted (Roehrig et al., 2013).

Consistent timing of the southward ITCZ shift during the decline period in CMIP3 and CMIP5 historical simulations supports the role of external forcing, chiefly anthropogenic aerosol (Hwang et al., 2013). The evolution of the observed decline and recovery is largely followed by the CMIP5 multi-model mean, further supporting the role of external drivers (Giannini and Kaplan, 2019). Updated results from CMIP6 for historical simulations with all and single forcings are represented in Figure 10.11d,e showing smaller trends than those observed. Giannini and Kaplan (2019) attempted to unify the driving mechanisms for decline and recovery based on singular-value decomposition of observed and modelled SSTs. Since the 1950s, tropical warming arising from GHGs and North Atlantic cooling from aerosol led to regional stabilization, suppressing Sahel rainfall. The subsequent reduction in aerosol emissions then led to North Atlantic warming and recovery of Sahel rainfall. Such mechanisms continue into the near-term future in idealized and modified RCP experiments, with scenarios featuring more aggressive reductions in aerosol emissions, or including aerosol–cloud interactions, favouring a greater northward shift of rainfall (Allen, 2015; Westervelt et al., 2017, 2018; Scannell et al., 2019). There is paleoclimate evidence of changes to Sahel rainfall in the past, in particular with enhancement of the West African monsoon during the mid-Holocene. However, the mechanisms governing such a change have been shown to be largely dynamical in nature (D’Agostino et al., 2019), suggesting that the mid-Holocene cannot be used to inform the credibility of changes due to greenhouse warming.

There is very high confidence (robust evidence and high agreement) that patterns of 20th-century ocean and land surface temperature variability have caused the Sahel drought and subsequent recovery by adjusting meridional gradients. There is high confidence (robust evidence and medium agreement) that the changing temperature gradients that perturb the West African monsoon and Sahel rainfall are themselves driven by anthropogenic emissions: warming by GHG emissions was initially restricted to the tropics but suppressed in the North Atlantic due to nearby emissions of sulphate aerosols, leading to a reduction in rainfall. The North Atlantic subsequently warmed following the reduction of aerosol emissions, leading to rainfall recovery.

The positive trend of precipitation has also been attributed to anthropogenic GHGemissions and stratospheric ozone depletion. CMIP5 models only show a positive trend when including anthropogenic forcings (Vera and Díaz, 2015). These results were supported by Knutson and Zeng (2018) based on univariate detection/attribution analysis of annual mean trends for the 1901–2010 and 1951–2010 periods. However, the main features of summer mean precipitation and variability of South America are still not well-represented in all CMIP5 and CMIP6 models (Gulizia and Camilloni, 2015; Díaz and Vera, 2017; Díaz et al., 2021). This motivates the construction of ensembles that exclude the worst performing models (Section 10.3.3.4). The construction of ensembles of CMIP5 historical simulations with realistic representation of precipitation anomalies with opposite sign over south-eastern South America and eastern Brazil showed that the trend since the 1950s could be related to changes in precipitation characteristics only when simulations included anthropogenic forcings (Díaz and Vera, 2017). GHG emissions have been related to increased precipitation in south-eastern South America through three different mechanisms (Figure 10.12a). First, GHG warming induces a non-zonally uniform pattern of SST warming that includes a warming pattern over the Indian and Pacific oceans that excites wave responses over South America (Junquas et al., 2013). Zonally uniform SST patterns of warming alone lead to precipitation signals opposite to those observed in an AGCM (Junquas et al., 2013). Second, GHG radiative forcing drives an expansion of the Hadley cell so that its descending branch moves poleward from the region, generating anomalous ascending motion and precipitation (H. Zhang et al., 2016; Saurral et al., 2019). The third mechanism by which increased GHG can contribute to increased precipitation in the region is through a delay of the stratospheric polar vortex breakdown. As depicted in Figure 10.12a, both stratospheric ozone depletion and increased GHGs have contributed to the later breakdown of the polar vortex in recent decades (McLandress et al., 2010; Wu and Polvani, 2017; Ceppi and Shepherd, 2019). Mindlin et al. (2020) developed future atmospheric circulation storylines (Section 10.3.4.2, Box 10.2) for Southern Hemisphere mid-latitudes with the CMIP5 models and found that for south-eastern South America summer precipitation, increases are related to the late-spring breakdown of the stratospheric polar vortex. The connecting mechanism is through a lagged southward shift of the jet stream (Saggioro and Shepherd, 2019), which enhances cyclonic activity over the region (Wu and Polvani, 2017).

There is high confidence that South-Eastern South America summer precipitation has increased since the beginning of the 20th century. Since AR5, science has advanced in the identification of the drivers of the precipitation increase in South-Eastern South America since 1950, including GHG through various mechanisms, stratospheric ozone depletion and Pacific and Atlantic variability. There is high confidence that anthropogenic forcing has contributed to the South-Eastern South America summer precipitation increase since 1950, butvery low confidence on the relative contribution of each driver to the precipitation increase.

It has also been suggested that the ocean-controlled influence is limited and internal atmospheric variability has to be invoked to fully explain the observed history of drought on decadal time scales (Seager and Hoerling, 2014; Seager and Ting, 2017). From roughly 1980 to the present, the regional climate signals show an interesting mix between forced and internal variability. Lehner et al. (2018) used a dynamical adjustment method and large ensembles of coupled and SST-forced atmospheric experiments to suggest that the observed south-western North America rainfall decline mainly results from the effects of atmospheric internal variability, which is in part driven by a PDV-related phase shift in Pacific SST around 2000 (Figure 10.13b,c). Based upon four SMILEs (three using a GCM and another one an AGCM constrained by observed SSTs) and a CMIP6 multi-model suite constrained by observed external forcings, Figure 10.13 shows, in agreement with Lehner et al. (2018), that observed SSTs with their associated atmospheric response are the main drivers of the south-western North America precipitation decrease during the 1983–2014 period. Once aspects of the internal variability are removed by dynamical adjustment, the observed precipitation change signal and simulated anthropogenically-forced components look more similar (Lehner et al., 2018).

Importantly, as the AR6 assessment views the PDV as being mostly driven by internal variability (Section 3.7.6), the lines of evidence cited above suggest that the contribution of natural and anthropogenic forcings to the precipitation decline has a small amplitude. Unlike the precipitation deficit, the accompanying south-western North America warming is driven primarily by anthropogenic forcing from GHGs rather than atmospheric circulation variability and may help to enhance the drought through increased evapotranspiration (Knutson et al., 2013; Diffenbaugh et al., 2015; Williams et al., 2015, Williams et al., 2020; Lehner et al., 2018, 2020).

To conclude, there is high confidence (robust evidence and medium agreement) that most (>50%) of the anomalous atmospheric circulation that caused the south-western North America negative precipitation trend can be attributed to teleconnections arising from tropical Pacific SST variations related to PDV. There is high confidence (robust evidence and medium agreement) that anthropogenic forcing has made a substantial contribution (about 50%) to the south-western North America warming since 1980.

The robustness of regional-scale attribution differs strongly between temperature and precipitation changes. While the influence of anthropogenic forcing on regional temperature long-term change has been detected and attributed in almost all land regions, a robust detection and attribution of human influence on regional precipitation change has not yet fully occurred for many land regions (Section 10.4.3). Although the contribution of anthropogenic forcing to long-term regional precipitation change has been detected in some regions, a robust quantification of the contributions of different drivers remains elusive. The delayed emergence of the anthropogenic precipitation fingerprint with respect to temperature is likely due to the opposing sign of the fast and slow land precipitation forced responses and time-dependent SST change patterns (Sections 8.2.1 and Section 10.4.3), stronger internal variability (Section 10.3.4.3) as well as larger observational uncertainty (Section 10.2) and impact of model biases. The contribution of internal variability to the observed changes can also be very sensitive to the period length and level of spatial aggregation for the region under scrutiny (Section 4.4.1 and Cross-Chapter Box 3.1; Kumar et al., 2016). Finally, even in the case of temperature changes at multi-decadal time scale, internal variability can still be a substantial driver of regional changes due to cancellation between different external forcings (Nath et al., 2018).

To conclude, it is virtually certain (robust evidence and high agreement) that anthropogenic forcing has been a major driver of temperature change since 1950 in many sub-continental regions of the world. There is high confidence (robust evidence and medium agreement) that anthropogenic forcing has contributed to multi-decadal mean precipitation changes in several regions, for example western Africa, south-east South America, south-western Australia, northern central Eurasia, and South and East Asia. However, at regional scale, the role of internal variability is stronger while uncertainties in observations, models and external forcing are all larger than at the global scale, precluding a robust assessment of the magnitude of the relative contributions of greenhouse gases, including stratospheric ozone, and different aerosol species.

Regional climate projections are one key element of the multiple lines of evidence that are used for climate risk assessments as well as for adaptation and policy decisions at regional scales (Sections 10.3.3.9 and 10.5). Regional climate projections can be separated into two components: the regional-scale forced response or regional-scale climate sensitivity when normalized by the global mean temperature change (Seneviratne and Hauser, 2020) and the climate internal variability characterizing the future period or global warming level under scrutiny. This section assesses a few methodological aspects related to robustness and emergence properties of the regional-scale forced response as well as the possible influence of internal variability on the emergence of the anthropogenic signal.

Figure 10.14 illustrates a similar comparison based on the CMIP6 multi-model ensemble forced with the scenario SSP5-8.5 and applied to two large-scale continental areas. The forced response to anthropogenic forcing is simply taken as the CMIP6 multi-model mean of future regional climate change relative to the 1850–1900 reference period. Robustness of the forced response is based on both significance of the change and model agreement about the sign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). Caution has to be exercised against a too literal interpretation of lack of robust change given that significance and sign agreement can be sensitive to spatial and temporal aggregation (Cross-Chapter Box Atlas.1, Figure 2) and lack of a robust change does not necessarily translate to lack of regional-scale climate change impacts (McSweeney and Jones, 2013; Hibino and Takayabu, 2016).

There is high confidence that the time-evolving contribution of different mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the regional-scale response of precipitation, to anthropogenic forcing. These mechanisms include non-linear temperature, precipitation and soil-moisture feedbacks, and slow and fast response of SST patterns and atmospheric circulation changes to increasing GHGs.

This section provides an assessment of the different approaches used in emergence studies as well as sensitivities to methodological choices. The section then focuses on the possible influence of internal variability on future emergence of the simulated mean precipitation anthropogenic signal at regional scales with some illustrative examples.

In climate science, emergence or distinguishability of a signal refers to the appearance of a persistent change in the probability distribution and/or temporal properties of a climate variable compared with that of a reference period (Section 1.4.2; Giorgi and Bi, 2009; Mahlstein et al., 2011, 2012; Hawkins and Sutton, 2012). Similar to anthropogenic climate change detection (Cross-Working Group Box: Attribution in Chapter 1), signal emergence can be detected, at least initially, without identifying the physical causes of the emergence (Section 1.4.2). In the context of human influence on climate, the objective of emergence studies is the search for the appearance of a signal characterizing an anthropogenically-forced change relatively to the climate variability of a reference period, defined as the noise.

Precise definitions of signal and noise as well as a metric to measure the relative importance of the signal are key ingredients of the emergence framework and depend on the framing question. In particular, emergence study results can depend on the specific definitions of signal and noise such as the level of spatial and temporal aggregation (McSweeney and Jones, 2013). For instance, grid-point scale emergence will likely be delayed compared with region-average emergence (Section 11.2.4 and Cross-Chapter Box Atlas.1, Figure 2; Fischer et al., 2013; Maraun, 2013b; Lehner et al., 2017a). The signal is often estimated by a running mean multi-decadal average or probability distribution function of the physical variable under scrutiny in order to avoid false emergence due to manifestation of multi-decadal internal variability (King et al., 2015). In the case of extremes such as climate records, a notion of multi-year persistence or recurrence can also be used to fully characterize the anthropogenic signal and its emergence (Christiansen, 2013; Bador et al., 2016).

Since AR5, the development and production of SMILEs (Sections 4.2.5 and 10.3.4.3) has allowed the assessment of the influence of internal variability on anthropogenic signal emergence. The influence of internal variability, and specifically of the unforced atmospheric circulation, on temperature signal emergence can delay or advance the time of emergence by a decade or two in mid- to high-latitude regions (Lehner et al., 2017a; Koenigk et al., 2020). Internal variability can also result in small or decreasing decadal to multi-decadal heatwave frequency trends under the historical anthropogenic forcing over most regions, thereby delaying emergence of unprecedented heatwave frequency trends relative to the pre-industrial trend distribution (Sections 11.2–11.3; Perkins-Kirkpatrick et al., 2017).

There is high confidence that consistency in definitions of signal and noise, choice of the reference period and signal-to-noise threshold, is important to robustly assess the future emergence of anthropogenic signals across different types or generations of models, as well as comparing past emergence results between observations and models. There is high confidence that internal variability can delay the emergence of the regional-scale mean precipitation anthropogenic signal in many regions, mainly located in the tropics, subtropics and mid-latitudes. An accurate estimation of the delay in regional-scale emergence caused by internal variability remains challenging due to global model biases in their representation of internal variability as well as methodological difficulties to precisely estimate these biases (high confidence).

Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches that can be used to construct climate information (Section 10.5 and Figure 10.1). Such data sources and methodologies include theoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple datasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general circulation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g., Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and single-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale anthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge).

Global warming has contributed to drying in dry summer climates including the Mediterranean (high confidence). Records of soil moisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean aridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the Mediterranean region (high confidence) (Section 8.3.1.6).

Cape Town’s crisis resulted from a combination of a strong, rare multi-year meteorological drought (Figure 10.18), estimated at 1 in 300 years (Wolski, 2018), and factors related to the nature of the water supply system, operational water management and water resource policies. Cape Town was very successful in implementing water-saving actions after the previous drought of 2000–2003, reducing water losses from over 22% to 15% (Frame and Killick, 2007; DWA, 2013), breaking the previous coupling of growth in water demand with growth in population. As a consequence, Cape Town won a Water Smart City award from the C40 Cities program only three years prior to the crisis. However, the water-saving actions, together with changing priorities in water resource provision from infrastructure-oriented towards resource and demand management, may well have led to delays in implementation of the expansion of water supply infrastructure (Muller, 2018). The expansion plan, formulated a decade prior to the crisis, included an expectation of long-term climate-change drying in the region (DWAF, 2007). The crisis also exposed structural deficiencies of water management and inadequacy of a policy process in which decisions about local water resources are taken at a national level, particularly in a situation of political tension (Visser, 2018). The crisis was widely seen as a harbinger of future problems to be faced by the city, and a highlight of vulnerability of many cities in the world resulting from the interplay of three factors: (i) the fast urban-population growth, (ii) the economic, policy, infrastructural and water resource paradigms and constraints, and (iii) anthropogenic climate change.

Cape Town is located at the south-western tip of Africa, within an approximately 100 km × 300 km region that receives 80% of its rainfall during the austral winter (March to October), with the largest portion in June to August. In the vicinity of Cape Town, rainfall is strongly heterogeneous, ranging from about 300 mm/year on coastal plains to >2,000 mm/year in mountain ranges. The Cape Town water supply relies on surface water reservoirs located in a few small mountain catchments (about 800 km² in total). The Cape Town region receives 85% of its rainfall from a series of cold fronts forming within mid-latitude cyclones. The remainder is brought in by infrequent cut-off lows that occur throughout the year (Favre et al., 2013). This creates a very strong water resource dependency on a single rainfall delivery mechanism that may be strongly affected by anthropogenic climate change (Chapter 4 and Section 10.6.2.6).

Global models show strong consistency in a drying signal for the Cape Town region, with the reduction in total annual rainfall of up to 20% by the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 simulations (Figure 10.18; Almazroui et al., 2020c). The consistency across the models is a robust signal compared to the rest of southern Africa, where the climate change signal varies spatially: stronger drying in the west and moderate drying or weak wetting in the east (DEA, 2013, 2018; see Atlas.4.4 for further discussion of southern Africa precipitation projections). Rainfall changes projected for the Cape Town region are consistent with projected changes in hemispheric-scale processes and regional-scale dynamics that point toward reduced frequency of frontal systems affecting that region. These changes include robust signals in CMIP5 models for the Southern Hemisphere for a poleward expansion of the tropics (Hu et al., 2013b), poleward displacement of mid-latitude storm tracks (Chang et al., 2012), increased strength and poleward shift of the westerly winds (Bracegirdle et al., 2018) and subtropical jet-streams (Chenoli et al., 2017), and a shift toward a more positive phase of the SAM (E.-P. Lim et al., 2016). However, despite the consistency in circulation changes, the emergence of anthropogenic rainfall change above unforced variability in West Southern Africa remains uncertain for annual rainfall throughout most of the 21st century, even under SSP5-8.5 (Figure 10.15).

Thus, although a clear association appears in observations from 1979 onward between increasing GHG concentrations, drying in the Cape Town region and behaviour of a key circulation process, the SAM, further analysis suggests caution. Not all global models show the historical post-1979 association among these factors, and when the observational record is extended back further to times when the anthropogenic greenhouse forcing was weaker, there is no strong association between the SAM and Cape Town drought. Thus, there is only medium confidence in the expectation of a future drier climate for Cape Town.

The Indian summer monsoon provides 80% of the country’s annual rainfall from June to September, supplying the majority of water for agriculture, industry, drinking and sanitation to over a billion people. Any variations in the monsoon on time scales from days to decades can have large impacts (Challinor et al., 2006; Gadgil and Gadgil, 2006). Evidence from paleoclimate records (Sections 8.3.2.4.1) shows high confidence in a weakened Indian monsoon during cold epochs of the past such as the Younger Dryas (12,800–11,600 years ago) as measured by speleothem oxygen isotopes (Kathayat et al., 2016). There is a pressing need to understand if the monsoon will change in the future under anthropogenic forcing and to quantify such changes. Multiple datasets have shown robust negative trends since the 1950s until the turn of the century (Bollasina et al., 2011) followed by a recovery (Jin and Wang, 2017), yet repeated assessments project the monsoon to increase in strength under enhanced GHG forcing (Christensen et al., 2007, 2013; Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between future projections and observed historical trends makes the region an ideal choice for an in-depth assessment. The reader is also referred to the South Asia (SAS) regional assessment of precipitation extremes (Section 11.9), which is not discussed here for brevity.

The robust decline of Indian summer monsoon rainfall averaged over India in the second half of the 20th century (Section 10.6.3.3) is not in line with expectations arising from thermodynamic constraints on the water cycle in a warming world (Section 8.2.2) and has been regarded as a puzzle (Goswami et al., 2006). Assessing the attribution of 20th-century changes to Indian rainfall is the subject of coordinated modelling under the Global Monsoon MIP (GMMIP; Zhou et al., 2016), but is complicated by long-standing dry biases in coupled CMIP3, CMIP5 (Sperber et al., 2013) and CMIP6 (Figure 10.19b) global models. These dry biases are connected to a lower tropospheric circulation that is too weak (Sperber et al., 2013) and wet biases in the equatorial Indian Ocean (Bollasina and Ming, 2013). Section 8.3.2.4.1 finds high confidence that anthropogenic aerosol emissions have dominated the observed declining trends of countrywide Indian summer monsoon rainfall, consistent with findings at the global-monsoon scale (Section 3.3.3.2).

Stronger Northern Hemisphere aerosol emissions cool it relative to the Southern Hemisphere, increasing northward energy transport at the expense of moisture transport towards India (Bollasina et al., 2011). The attribution to anthropogenic aerosols is supported in CMIP5 single-forcing experiments, including some testing the sensitivity to local and remote emissions (Guo et al., 2015, 2016; Shawki et al., 2018), comparing CMIP5 GCMs forced by both aerosol and GHG to GHG only (Salzmann et al., 2014) and reducing emissions to pre-industrial levels (Takahashi et al., 2018). The large spread between individual model realisations of comparable magnitude to the aerosol-induced signal suggested to Salzmann et al. (2014) that internal variability may also play a role over regions such as northern-central India. Further uncertainty surrounds the level of radiative forcing. Dittus et al. (2020) forced a GCM with historical aerosol emissions scaled between 0.2 and 1.5 times their observed values, representing the spread in CMIP5 effective radiative forcing. The strongest forcing led to around 0.5 mm day^–1less late-20th century Indian monsoon rainfall than the weakest (Shonk et al., 2020). Meanwhile, the uncertainty surrounding aerosol–cloud interactions could change the sign of long-term precipitation trends (Takahashi et al., 2018).

A small anthropogenic contribution may be expected from local land-use/land-cover changes and land management. India is the world’s most irrigated region with around 0.5 mm/day in places, although peaking higher in summer (Cook et al., 2015b; McDermid et al., 2017). Including irrigation in GCMs and RCMs slows the monsoon circulation and diminishes rainfall (Lucas-Picher et al., 2011; Guimberteau et al., 2012; Shukla et al., 2014; Tuinenburg et al., 2014; Cook et al., 2015b) due to reduced surface temperature (Thiery et al., 2017), reducing the monsoon wind and moisture fluxes towards India (Mathur and AchutaRao, 2020). However, implementation methodologies for irrigation in climate models are simplified and often do not account for spatial heterogeneity while overestimating demand and supply (Section 10.3.3.6; Nazemi and Wheater, 2015; Pokhrel et al., 2016). Changing forest cover to agricultural land in an RCM (Paul et al., 2016) finds weakened summer monsoon rainfall especially in central and eastern India, due to decreased local evapotranspiration. Decreased evapotranspiration from a warmer surface since the 1950s in the CMIP5 ensemble may also feedback on the supply of moisture (Ramarao et al., 2015). Based on an AGCM study and literature review, Krishnan et al. (2016) support the role of land-use/land-cover change in adding to the effects of aerosol in weakening the monsoon, in addition to dynamic effects on the circulation caused by rapid warming of the Indian Ocean.

In addition to anthropogenic forcing, there is evidence that internal variability in the Pacific is a significant driver. Huang et al. (2020b) compared a large perturbed-physics ensemble (HadCM3C) with a SMILE for the historical period. Ensemble members replicating the negative Indian rainfall trend were accompanied by a strong phase change in the PDV from negative to positive, consistent with SST observations. Jin and Wang (2017) have demonstrated increasing Indian monsoon rainfall since 2002 in a variety of observed datasets, suggesting the increase is due either to a change in dominance of a particular forcing (for example from aerosol to GHG) or to a change in phase of internal variability such as the PDV. Huang et al. (2020b) partially attribute the rainfall recovery to a phase change in the PDV, supported by a SMILE study combined with reanalyses (Ha et al., 2020).

The drying trend of Indian summer monsoon rainfall since the mid-20th century can be attributed with high confidence to aerosol as the dominant anthropogenic forcing with a further contribution from internal variability, supported by the review of B. Wang et al. (2021) including CMIP6 results. Understanding the interplay between anthropogenic and internal drivers will be important for understanding future change.

Given the assessment for a future wetter monsoon dominated by GHG emissions and attribution of the late-20th century decline to aerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between dominant forcings will lead, at some point, to a positive trend. For example, RCP4.5 experiments in an AGCM forced by coupled model-derived future SSTs showed continuation of 20th-century drying, before a rainfall recovery (Krishnan et al., 2016). By holding aerosol emissions at 2005 levels, lower monsoon rainfall is found throughout the 21st century than in a standard RCP8.5 scenario (Zhao et al., 2019), suggesting that the timing of the recovery will be partially controlled by the rate at which aerosol emissions decline. The spread in spatial distribution of aerosol emissions in SSPs may also play a role in near-term projections (Samset et al., 2019). Under divergent air-quality policies, SSP3 features a dipole of declining sulphate emissions for China but increases over India, leading to suppression of GHG-related precipitation increases there (Wilcox et al., 2020). For the near-term future around the mid-21st century, the interplay between internal variability and external forcing must be considered (Singh and AchutaRao, 2019). Huang et al. (2020a) used two SMILEs to show that internal variability related to PDV could potentially overcome the GHG-forced upward trend in Indian monsoon rainfall, consistent with assessments of the global monsoon for the near term (Section 4.4.1.4). Emergence of the anthropogenic signal for South Asian precipitation is shown from the 2050s onwards in CMIP6 (Figure 10.15b).

Projections can also be expressed in terms of global-mean warming levels (GWLs) rather than time horizons (Cross-Chapter Box 11.1). Advancing on SR1.5, amplification of mean and extreme monsoon rainfall at 2.0°C compared to 1.5°C has been found both by an AGCM forced by future SST patterns (Chevuturi et al., 2018) and by using time slices in CMIP5 GCMs (Yaduvanshi et al., 2019; J. Zhang et al., 2020). These findings are consistent with the general scaling of Indian monsoon precipitation per degree of warming in CMIP5 (Zhang et al., 2019) and CMIP6 (B. Wang et al., 2021). Increasing GWLs also lead to emergence of the anthropogenic signal over larger proportions of the South Asian region (Figure 10.15a).

Finally, the recent national climate-change assessment for India (Krishnan et al., 2020) has distilled multiple lines of evidence to show declining summer monsoon rainfall over the second half of the 20th century, attributable to emissions of anthropogenic aerosols, while future projections informed by CMIP5 modelling and dominated by GHG forcing show increased mean rainfall by the end of the 21st century.

There is very high confidence (robust evidence, high agreement) of a negative trend of summer monsoon rainfall over the second half of the 20th century averaged over all of India. There is medium agreement over trends at the regional level owing to uncertainty among observational products, which hinders model evaluation, downscaling and assessment of changes to extremes. There is high confidence (robust evidence, medium agreement) that anthropogenic aerosol emissions over the Northern Hemisphere and internal variability have contributed to the negative trend, while there is high confidence (robust evidence, medium agreement) that Indian summer monsoon rainfall will increase at the end of the 21st century in response to increased GHG forcing, due to the dominance of thermodynamic mechanisms. No contradictory evidence is found from downscaling methods. The contrast between declining rainfall in the observational record and long-term future increases can be explained using multiple lines of evidence. They are not contradictory since they are attributable to different mechanisms (primarily aerosols and greenhouse gases, respectively). The long-term future changes are generally consistent across global (including at high resolution) and regional climate models, and supported by theoretical arguments. Furthermore, while there are subtle differences found in past periods with a climate similar to the future climate (the mid-Holocene), different physical mechanisms at play suggest that paleoclimate evidence does not reduce confidence in the future projections. In the near term, there is high confidence that internal variability will dominate.

The Mediterranean summer climate is affected by large-scale modes of natural variability, the most dominant being the NAO (Annex IV) in winter and the summer NAO in summer (Folland et al., 2009; Bladé et al., 2012), although regional differences exist. The influence of those modes of variability over the eastern Mediterranean is recognized by some studies (Chronis et al., 2011; Kahya, 2011; Black, 2012; Bladé et al., 2012), but disputed by others (Ben-Gai et al., 2001; Ziv et al., 2006; Donat et al., 2014; Turki et al., 2016; Zamrane et al., 2016; Han et al., 2019). During positive summer NAO phase, associated with an upper-level trough over the Balkans, the Mediterranean is anomalously wet (Bladé et al., 2012). Drivers of Mediterranean climate variability include modes of variability such as the AMV (Sutton and Dong, 2012) and the Asian monsoon (Rodwell and Hoskins, 1996; Logothetis et al., 2020). In addition, the increase of GHGs (e.g., Zittis et al., 2019), the decrease of anthropogenic aerosols over Europe and the Mediterranean since the 1980s resulting from air pollution policies (Turnock et al., 2016), and anthropogenic land-use change (Millán, 2014; MedECC 2020) have been shown to be linked to the regional warming. The role of the zonal averaged circulation as a driver for the Mediterranean climate has been stressed by (Garfinkel et al., 2020).

The observed trends over 1901–2010 are outside the range of internal variability shown in CMIP5 pre-industrial control experiments and consistent with, or greater than those simulated by experiments including both anthropogenic and natural forcings (Knutson et al., 2013) and therefore partly attributable to anthropogenic forcing. The decrease of anthropogenic aerosols over Europe including the Mediterranean resulting from European de-industrialisation and air pollution policies (Turnock et al., 2016) has been highlighted as an important contributor to the observed warming (Ruckstuhl et al., 2008; Philipona et al., 2009; de Laat and Crok, 2013; Nabat et al., 2014; Besselaar et al., 2015; Dong et al., 2017; Boé et al., 2020a). Pfeifroth et al. (2018) argue that this brightening is mainly due to cloud changes caused by the indirect aerosol effect with a minor role for the direct aerosol effect, in contrast to Nabat et al. (2014) and Boers et al. (2017) who attribute it to the direct aerosol effect. Using model sensitivity experiments, Nabat et al. (2014) also associated the increase in Mediterranean SST since 1980–2012 with the decrease in aerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5).

As noted in Section 10.6.4.4, the Mediterranean summer climate is affected by large-scale circulation patterns, of which the summer NAO is the most important (Folland et al., 2009; Bladé et al., 2012). Barcikowska et al. (2020) highlight the importance of correctly simulating the summer NAO impact on the Mediterranean climate, as it partly offsets the anthropogenic warming signal in the western and central Mediterranean.

Despite the large efforts of these regional downscaling projects, the global model–RCM matrix is still sparse and lacking a systematic design to explore the uncertainty sources (e.g., global model, RCM, scenario, resolution) (Section 10.3). Focusing on the Iberian peninsula, Fernández et al. (2019) argued that the driving global model is the main contributor to uncertainty in the ensemble. Physically consistent but implausible temperature changes in RCMs can occur. An example is a strong temperature increase over the Pyrenees due to excessive snow cover in the present climate (Fernández et al., 2019). Based on an older set of RCM simulations (ENSEMBLES), Déqué et al. (2012) also argued that the largest source of uncertainty in the temperature response over southern Europe is the choice of the driving global model (whereas for summer precipitation the choice of the RCM dominates the uncertainty). Finally, Boé et al. (2020a) found that over a large area of Europe, including parts of the Mediterranean, RCMs project a summer warming 1.5°C–2°C colder than in their driving global models for the end of the 21st century. This is caused by differences in solar radiation related to the absence of time-varying anthropogenic aerosols in RCMs (Boé et al., 2020a; Gutiérrez et al., 2020), which also affects the noted differences in cloud cover between global models and RCMs (Bartók et al., 2017).

During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the nighttime canopy urban heat island effect (Bader et al., 2018; Kuang, 2019; Li et al., 2019; Y. Li et al., 2020a). While green and blue infrastructures can mitigate the urban heat island effect, three main factors contribute to its development (Hamdi et al., 2020; Masson et al., 2020): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height; (ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, especially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism (Ichinose et al., 1999; Sailor, 2011; de Munck et al., 2013; Bohnenstengel et al., 2014; Chow et al., 2014; Salamanca et al., 2014; Dou and Miao, 2017; Ma et al., 2017a; Chrysoulakis et al., 2018; Takane et al., 2019). Urban heat island magnitude is also affected by aerosols due to air pollution in urban areas (Cheng et al., 2020; Han et al., 2020) and by local background climate (Zhao et al., 2014; Ward et al., 2016).

Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface-atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished (Masson, 2006; Grimmond et al., 2010, 2011; Chen et al., 2011; Best and Grimmond, 2015): (i) in the slab or bulk approach, the three-dimensional city structure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing roughness length and displacement height (e.g., Seaman et al., 1989; Dandou et al., 2005; Best et al., 2006; Liu et al., 2006). The energy balance is often modifiedto account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic heat fluxes. (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and wall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy fluxes (Masson, 2000; Kusaka et al., 2001). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct interaction with the planetary boundary layer (Brown, 2000; Martilli et al., 2002; Hagishima et al., 2005; Dupont and Mestayer, 2006; Hamdi and Masson, 2008; Schubert et al., 2012). Building-energy models that estimate anthropogenic heat from a building for given atmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban vegetation (Lee et al., 2016; Redon et al., 2017; Mussetti et al., 2020).

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The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low confidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

Aerosol forcing also has a strong regional footprint associated with regional emissions, which affects temperature and precipitation extremes (high confidence) (Sections 11.3 and 11.4). From around the 1950s to 1980s, enhanced aerosol loadings led to regional cooling due to decreased global solar radiation (‘global dimming’) which was followed by a phase of ‘global brightening’ due to a reduction in aerosol loadings (Chapters 3 and 7; Wild et al., 2005). King et al. (2016b) show that aerosol-induced cooling delayed the timing of a significant human contribution to record-breaking heat extremes in some regions. However, the decreased aerosol loading since the 1990s has led to an accelerated warming of hot extremes in some regions. Based on Earth system model (ESM) simulations, Dong et al. (2017) suggest that a substantial fraction of the warming of the annual hottest days in Western Europe since the mid-1990s has been due to decreases in aerosol concentrations in the region. Dong et al. (2016b) also identify non-local effects of decreases in aerosol concentrations in Western Europe, which they estimate played a dominant role in the warming of the hottest daytime temperatures in north-east Asia since the mid-1990s, via induced coupled atmosphere–land surface and cloud feedbacks, rather than a direct impact of anthropogenic aerosol changes on cloud condensation nuclei.

However, since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of literature (see series of supplements to the annual State of the Climate report (Peterson et al., 2012, 2013a; Herring et al., 2014, 2015, 2016, 2018), including the number of approaches to examining extreme events(described in Easterling et al., 2016; Otto, 2017; Stott et al., 2016)). A commonly used approach – often called the risk-based approach in the literature, and referred to here as the ‘probability-based approach’ – produces statements such as ‘anthropogenic climate change made this event type twice as likely ’ or ‘anthropogenic climate change made this event 15% more intense’. This is done by estimating probability distributions of the index characterizing the event in today’s climate, as well as in a counterfactual climate, and either comparing intensities for a given occurrence probability (e.g., 1-in-100-year event) or probabilities for a given magnitude (see FAQ 11.3). There are a number of different analytical methods encompassed in the probability-based approach, building on observations and statistical analyses (e.g., van Oldenborgh et al., 2012), optimal fingerprint methods (Sun et al., 2014), regional climate and weather forecast models (e.g., Schaller et al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly, 2013), and large ensembles of atmosphere-only GCMs (e.g., Lott et al., 2013). A key component in any event attribution analysis is the level of conditioning on the state of the climate system. In the least conditional approach, the combined effect of the overall warming and changes in the large-scale atmospheric circulation are considered and often utilize fully coupled climate models (Sun et al., 2014). Other more conditional approaches involve prescribing certain aspects of the climate system. These range from prescribing the pattern of the surface ocean change at the time of the event (e.g., Hoerling et al., 2013, 2014), often using Atmospheric Model Intercomparison Project (AMIP) style global models, where the choice of sea surface temperature and ice patterns influences the attribution results (Sparrow et al., 2018), to prescribing the large-scale circulation of the atmosphere and using weather forecasting models or methods (e.g., Pall et al., 2017; Patricola and Wehner, 2018; Wehner et al., 2018a). These highly conditional approaches have also been called ‘storylines’ (Cross-Working Group Box 1.1; Shepherd, 2016) and can be useful when applied to extreme events that are too rare to otherwise analyse, or where the specific atmospheric conditions were central to the impact. These methods are also used to enable the use of very high-resolution simulations in cases were lower-resolution models do not simulate the regional atmospheric dynamics well (Shepherd, 2016; Shepherd et al., 2018). However, the imposed conditions limit an overall assessment of the anthropogenic influence on an event, as the fixed aspects of the analysis may also have been affected by climate change. For instance, the specified initial conditions in the highly conditional hindcast attribution approach often applied to tropical cyclones (e.g., Takayabu et al., 2015; Patricola and Wehner, 2018) permit only a conditional statement about the magnitude of the storm if similar large-scale meteorological patterns could have occurred in a world without climate change, thus precluding any attribution statement about the change in frequency if used in isolation. Combining conditional assessments of changes in the intensity with a multi-model approach does allow for the latter as well (Shepherd, 2016).

Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and seasonal mean values and extremes of different climate and impact variables across different multi-model ensembles and/or different scenarios (e.g., Frieleret al. , 2012; Scheweet al. , 2014; Hergeret al. , 2015; Schleussneret al. , 2016; Seneviratneet al. , 2016; Wartenburgeret al. , 2017; Bettset al. , 2018; Dosio and Fischer, 2018; Samset et al. , 2019; Tebaldiet al. , 2020; see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrasset al. , 2015; Seneviratneet al. , 2016; Wartenburgeret al. , 2017; Seneviratne and Hauser, 2020). The consistency tends to be higher for temperature-related variables than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions scenarios than for low-emissions scenarios (e.g., for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 scenario: Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at a given GWL are smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response pattern is further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h).

Regional external forcings, including land-use changes and emissions of anthropogenic aerosols, play an important role in the changes of temperature extremes in some regions (high confidence) (Section 11.1.6). Deforestation may have contributed to about one third of the warming of hot extremes in some mid-latitude regions since the pre-industrial time (Lejeune et al., 2018). Aspects of agricultural practice, including no-till farming, irrigation, and overall cropland intensification, may cool hot temperature extremes (Davin et al., 2014; N.D. Mueller et al., 2016). For instance, cropland intensification has been suggested to be responsible for a cooling of the highest temperature percentiles in Midwest USA (N.D. Mueller et al., 2016). Irrigation has been shown to be responsible for a cooling of hot temperature extremes of up to 1°C–2°C in many mid-latitude regions in the present climate (Thieryet al., 2017, 2020), a process not represented in most of state-of-the-art ESMs (CMIP5, CMIP6). Double cropping may have led to increased hot extremes in the inter-cropping season in part of China (Jeong et al., 2014). Rapid increases in summer warming in western Europe and north-east Asia since the 1980s are linked to a reduction in anthropogenic aerosol precursor emissions over Europe (Nabat et al., 2014; Dong et al., 2016b, 2017), in addition to the effect of increased greenhouse gas forcing (see also Section 10.1.3.1). This effect of aerosols on temperature-related extremes is also noted for declines in short-lived anthropogenic aerosol emissions over North America (Mascioli et al., 2016). On the local scale, the urban heat island (UHI) effect results in higher temperatures in urban areas than in their surrounding regions, and contributes to warming in regions of rapid urbanization, in particular for nighttime temperature extremes (Box 10.3; Phelan et al., 2015; Chapman et al., 2017; Y. Sun et al., 2019). But these local and regional forcings are generally not or not well represented in the CMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing to uncertainty in model simulated changes.

In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes. At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and modes of variability, feedbacks including soil-moisture–evapotranspiration–temperature or snow/ice–albedo–temperature feedbacks, and local and regional forcings such as land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. This leads to heterogeneity in regional changes and their associated uncertainties (high confidence). Changes in anthropogenic aerosol concentrations have likely affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA (medium confidence). Urbanization has likely exacerbated the effects of global warming in cities, in particular for nighttime temperature extremes.

The SREX (IPCC, 2012) assessed that it is likely anthropogenic influences have led to the warming of extreme daily minimum and maximum temperatures at the global scale. The AR5 concluded that human influence has very likely contributed to the observed changes in the intensity and frequency of daily temperature extremes on the global scale in the second half of the 20th century (IPCC, 2014). With regard to individual, or regionally or locally specific events, AR5 concluded that it is likely human influence has substantially increased the probability of occurrence of heatwaves in some locations.

Studies since AR5 continue to attribute the observed increase in the frequency or intensity of hot extremes and the observed decrease in the frequency or intensity of cold extremes to human influence, dominated by anthropogenic greenhouse gas emissions, on global and continental scales, and for many AR6 regions. These include attribution of changes in the magnitude of annual TXx, TNx, TXn, and TNn, based on different observational datasets including, HadEX2 and HadEX3, CMIP5 and CMIP6 simulations, and different statistical methods (Kim et al., 2016; Z. Wang et al., 2017a; Seong et al., 2021). As is the case for an increase in mean temperature (Section 3.3.1), an increase in extreme temperature is mostly due to greenhouse gas forcing, offset by aerosol forcing. The aerosols’ cooling effect is clearly detectable over Europe and Asia (Seong et al., 2021). As much as 75% of the moderate daily hot extremes (above 99.9th percentile) over land are due to anthropogenic warming (Fischer and Knutti, 2015). New results are found to be more robust due to the extended period that improves the signal-to-noise ratio. The effect of anthropogenic forcing is clearly detectable and attributable in the observed changes in these indicators of temperature extremes, even at country and sub-country scales, such as in Canada (Wan et al., 2019). Changes in the number of warm nights, warm days, cold nights, and cold days, and other indicators such as the Warm Spell Duration Index (WSDI), are also attributed to anthropogenic influence (Christidis and Stott, 2016; Hu et al., 2020).

Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), Australia (Alexander and Arblaster, 2017), and Europe (Christidis and Stott, 2016), found similar results. A clear anthropogenic signal is also found in the trends in the Combined Extreme Index (CEI) for North America, Asia, Australia, and Europe (Dittus et al., 2016). While various studies have described increasing trends in several heatwave metrics (heatwave duration, the number of heatwave days, etc.) in different regions (e.g., Cowan et al., 2014; Bandyopadhyay et al., 2016; M. Sanderson et al., 2017), few recent studies have explicitly attributed these changes to causes; most of them stated that observed trends are consistent with anthropogenic warming. The detected anthropogenic signals are clearly separable from the response to natural forcing, and the results are generally insensitive to the use of different model samples, as well as different data availability, indicating robust attribution. Studies of monthly, seasonal, and annual records in various regions (Kendon, 2014; Lewis and King, 2015; Bador et al., 2016; Meehl et al., 2016; C. Zhou et al., 2019) and globally (King, 2017) show an increase in the breaking of hot records and a decrease in the breaking of cold records (King, 2017). Changes in anthropogenically attributablerecord-breaking rates are noted to be largest over the Northern Hemisphere land areas (Shiogama et al., 2016). Yin and Sun (2018) found clear evidence of an anthropogenic signal in the changes in the number of frost and ice days, when multiple model simulations were used. In some key wheat-producing regions of Southern Australia, increases in frost days or frost season length have been reported (Dittus et al., 2014; Crimp et al., 2016); these changes are linked to decreases in rainfall, cloud-cover, and subtropical ridge strength, despite an overall increase in regional mean temperatures (Dittus et al., 2014; Pepler et al., 2018).

A significant advance since AR5 has been a large number of studies focusing on extreme temperature events at monthly and seasonal scales, using various extreme event attribution methods. Diffenbaugh et al. (2017) found that anthropogenic warming has increased the severity and probability of the hottest month by more than 80% of the available observational area on the global scale. Christidis and Stott (2014) provide clear evidence that warm events have become more probable because of anthropogenic forcings. Sun et al. (2014) found that human influence has caused a more than 60-fold increase in the probability of the extreme warm 2013 summer in eastern China since the 1950s. Human influence is found to have increased the probability of the historically hottest summers in many regions of the world, both in terms of mean temperature (B. Mueller et al., 2016) and wet bulb globe temperature (WBGT; C. Li et al., 2017). In most regions of the Northern Hemisphere, changes in the probability of extreme summer average WBGT were found to be about an order of magnitude larger than changes in the probability of extreme hot summers estimated by surface air temperature (C. Li et al., 2017). In addition to these generalized, global-scale approaches, extreme event studies have found an attributable increase in the probability of hot annual and seasonal temperatures in many locations, including Australia (Knutson et al., 2014b; Lewis and Karoly, 2014), China (Sun et al., 2014; Sparrow et al., 2018; Zhou et al., 2020), Korea (Y.-H. Kim et al., 2018) and Europe (King et al., 2015b).

There have also been many extreme event attribution studies that examined short-duration temperature extremes, including daily temperatures, temperature indices, and heatwave metrics. Examples of these events from different regions are summarized in various annual Explaining Extreme Events supplements of the Bulletin of the American Meteorological Society (Peterson et al., 2012, 2013a; Herring et al., 2014, 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in Easterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies of recent events from 2016 onwards have determined an infinite risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that the occurrence probability for such events is close to zero in model simulations without anthropogenic influences (see Herring et al., 2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it is difficult to accurately estimate the lower bound of the uncertainty range of the FAR in these cases (Paciorek et al., 2018), the fact that those events are so far outside the envelop of the models with only natural forcing indicates that it is extremely unlikely for those events to occur without human influence.

Studies that focused on the attributable signal in observed cold extreme events show human influence reducing the probability of those events. Individual attribution studies on the extremely cold winter of 2011 in Europe (Peterson et al., 2012), in the eastern USA during 2014 and 2015 (Trenary et al., 2015, 2016; Wolter et al., 2015; Bellprat et al., 2016), in the cold spring of 2013 in the United Kingdom (Christidis et al., 2014), and of 2016 in eastern China (Qian et al., 2018; Y. Sun et al., 2018b) all showed a reduced probability due to human influence on the climate. An exception is the study of Grose et al. (2018), which found an increase in the probability of the severe western Australian frost of 2016 due to anthropogenically-driven changes in circulation patterns that drive cold outbreaks and frost probability.

The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due to differences in changes in atmospheric circulation, land–surface feedbacks, and other external drivers such as aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have been found (Kew et al., 2019; Vautard et al., 2020), whereas in the USA, changes are much less pronounced. This is probably a reflection of the land–surface feedback enhanced extreme 1930s temperatures that reduce the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming effect of greenhouse gases (Wehner et al., 2018c). Irrigation and crop intensification have been shown to lead to a cooling in some regions, in particular in North America, Europe, and India (high confidence) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen and Dirmeyer, 2019). Deforestation has contributed about one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times (Lejeune et al., 2018). Despite all of these differences, and larger uncertainties at the regional scale, nearly all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of hot extremes and to a decrease in the frequency or intensity of cold extremes.

Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, IPCC, 2014) concluded with medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. These assessments were based on the evidence of anthropogenic influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and limited evidence of anthropogenic influence on extreme precipitation of durations of one and five days.

Since AR5 there has been new and robust evidence and improved understanding of human influence on extreme precipitation. In particular, detection and attribution analyses have provided consistent and robust evidence of human influence on extreme precipitation of one- and five-day durations at global to continental scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951–2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to warming is consistent with C-C scaling (Zhang et al., 2013). This is confirmed to be robust when an additional nine years of observational data and the CMIP6 model simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik et al., 2020). The influence of greenhouse gases is attributed as the dominant contributor to the observed intensification. The global average of Rx1day in the observations is consistent with simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top fifth or top first percentiles of daily precipitation can also be attributed to human influence at the global scale (Dong et al., 2021). The CMIP5 models were able to capture the fraction of land experiencing a strong intensification of heavy precipitation during 1960–2010 under anthropogenic forcing, but not in unforced simulations (Fischer et al., 2014). But the models underestimated the observed trends (Borodina et al., 2017a). Human influence also significantly contributed to the historical changes in record-breaking one-day precipitation (Shiogama et al., 2016). There is also limited evidence of the influences of natural forcing. Substantial reductions in Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation intensity over the global summer monsoon regions occurred during 1957–2000 after explosive volcanic eruptions (Paik and Min, 2018). The reduction in post-volcanic eruption extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon circulation weakening) play important roles.

There has been new evidence of human influence on extreme precipitation at continental scales, including the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over North America, Eurasia, and mid-latitude land regions (Zhang et al., 2013) and of greenhouse gas forcing in Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions (Paik et al., 2020). These findings are corroborated by the detection of human influence in the fraction of extreme precipitation in the total precipitation over Asia, Europe, and North America (Dong et al., 2021). Human influence was found to have contributed to the increase in frequency and intensity of regional precipitation extremes in North America during 1961–2010, based on optimal fingerprinting and event attribution approaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) found the observed latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to anthropogenic forcing.

Evidence of human influence on extreme precipitation at regional scales is more limited and less robust. In north-west Australia, the increase in extreme rainfall since 1950 can be related to increased monsoonal flow due to increased aerosol emissions, but cannot be attributed to an increase in greenhouse gases (Dey et al., 2019a). Anthropogenic influence on extreme precipitation in China was detected in one study (H. Li et al., 2017), but not in another using different detection and data-processing procedures (W. Li et al., 2018a), indicating the lack of robustness in the detection results. A still weak signal-to-noise ratio seems to be the main cause for the lack of robustness, as detection would become robust 20 years in the future (W. Li et al., 2018a). Krishnan et al. (2016) attributed the observed increase in heavy rain events (intensity >100 mm day^–1) in the post-1950s over central India to the combined effects of greenhouse gases, aerosols, land-use and land-cover changes, and rapid warming of the equatorial Indian Ocean SSTs. Roxyet al. (2017) and Devanand et al. (2019) showed that the increase in widespread extremes over the South Asian Monsoon during 1950–2015 is due to the combined impacts of the warming of the Western Indian Ocean (Arabian Sea) and the intensification of irrigation water management over India.

Anthropogenic influence may have affected the large-scale meteorological processes necessary for extreme precipitation and the localized thermodynamic and dynamic processes, both contributing to changes in extreme precipitation events. Several new methods have been proposed to disentangle these effects by either conditioning on the circulation state or attributing analogues. In particular, the extremely wet winter of 2013–2014 in the UK can be attributed, approximately to the same degree, to both temperature-induced increases in saturation vapour pressure and changes in the large-scale circulation (Vautard et al., 2016; Yiou et al., 2017). There are multiple cases indicating that very extreme precipitation may increase at a rate more than the C-C rate (7% per 1°C of warming) (Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; van Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018).

Event attribution studies found an influence of anthropogenic activities on the probability or magnitude of observed extreme precipitation events, including European winters (Schaller et al., 2016; Otto et al., 2018b), extreme 2014 precipitation over the northern Mediterranean (Vautard et al., 2015), parts of the USA for individual events (Knutson et al., 2014a; Szeto et al., 2015; Eden et al., 2016; van Oldenborgh et al., 2017), extreme rainfall in 2014 over Northland, New Zealand (Rosier et al., 2015) or China (Burke et al., 2016; Sun and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). However, for other heavy rainfall events, studies identified a lack of evidence about anthropogenic influences (Imada et al., 2013; Schaller et al., 2014; Otto et al., 2015c; Siswanto et al., 2015). There are also studies where results are inconclusive because of limited reliable simulations (Christidis et al., 2013b; Angélil et al., 2016). Overall, both the spatial and temporal scales on which extreme precipitation events are defined are important for attribution; events defined on larger scales have larger signal-to-noise ratios and thus the signal is more readily detectable. At the current level of global warming, there is a strong enough signal to be detectable for large-scale extreme precipitation events, but the chance of detecting such signals for smaller-scale events decreases (Kirchmeier-Young et al., 2019).

In summary, most of the observed intensification of heavy precipitation over land regions is likely due to anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and robust evidence since AR5 includes attribution to human influence of the observed increases in annual maximum one-day and five-day precipitation and in the fraction of annual precipitation falling in heavy events. The evidence since AR5 also includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, both of which can only be explained when anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant (high confidence) (North America, Europe and Asia). On the spatial scale of AR6 regions, there is limited evidence of human influence on extreme precipitation, but new evidence is emerging; in particular, studies attributing individual heavy precipitation events found that human influence was a significant driver of the events, particularly in the winter season.

There are very few studies focused on the attribution of long-term changes in floods, but there are studies on changes in flood events. Most of the studies focus on flash floods and urban floods, which are closely related to intense precipitation events (Hannaford, 2015). In other cases, event attribution focused on runoff using hydrological models, and examples include river basins in the UK (Section 11.4.4; Schaller et al., 2016; Kay et al., 2018), the Okavango River in Africa (Wolski et al., 2014), and the Brahmaputra River in Bangladesh (Philip et al., 2019). Findings about anthropogenic influences vary between different regions and basins. For some flood events, the probability of high floods in the current climate is lower than in a climate without an anthropogenic influence (Wolski et al., 2014), while in other cases anthropogenic influence leads to more intense floods (Cho et al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al., 2018a; Teufel et al., 2019). Factors such as land-cover change and river management can also increase the probability of high floods (Ji et al., 2020). These, along with model uncertainties and the lack of studies overall, suggest a low confidence in general statements to attribute changes in flood events to anthropogenic climate change. A few individual regions have been well studied, which allows for high confidence in the attribution of increased flooding in these cases. For example, flooding in the UK following increased winter precipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed to anthropogenic climate change (Schaller et al., 2016; Vautard et al., 2016; Yiou et al., 2017; Otto et al., 2018b).

Attributing changes in heavy precipitation to anthropogenic activities (Section 11.4.4) cannot be readily translated to attributing changes in floods to human activities, because precipitation is only one of the multiple factors, albeit an important one, that affect floods. For example, Teufel et al. (2017) showed that, while human influence increased the odds of the flood-producing rainfall for the 2013 Alberta flood in Canada, it was not detected to have influenced the probability of the flood itself. Schaller et al. (2016) showed that human influence on the increase in the probability of heavy precipitation translated linearly into an increase in the resulting river flow of the Thames in the UK in winter 2014, but its contribution to the inundation was inconclusive.

Gudmundsson et al. (2021) compared the spatial pattern of the observed regional trends in high river flows (>90th percentile) over 1971–2010 with that simulated by global hydrological models. The hydrological models were driven by outputs of climate model simulations under all historical forcing and pre-industrial forcing conditions. They found complex spatial patterns of extreme river flow trends. They also found the observed spatial patterns of trends can be reproduced only if anthropogenic climate change is considered, and that simulated effects of water and land management cannot reproduce the observed spatial pattern of trends. As there is only one study and multiple caveats associated with the study, including relatively poor observational data coverage, there is low confidence about human influence on the changes in high river flows on the global scale.

Attribution studies for recent meteorological drought events are available for various regions. In Western and Central Europe, a multi-method and multi-model attribution study on the 2015 Central European drought did not find conclusive evidence for whether human-induced climate change was a driver of the rainfall deficit, as the results depended on model and method used (Hauser et al., 2017). In the Mediterranean region, a human contribution was found in the case of the 2014 meteorological drought in the southern Levant based on a single-model study (Bergaoui et al., 2015). In Africa, there is some evidence of a contribution of human emissions to single meteorological drought events, such as the 2015–2017 southern African drought (Funk et al., 2018a; Yuan et al., 2018a; Pascale et al., 2020), and the three-year (2015–2017) drought in the western Cape Town region of South Africa (Otto et al., 2018c). An attributable signal was not found in droughts that occurred in different years with different spatial extents in the last decade in North and South Eastern Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., 2018a; Philip et al., 2018b; Kew et al., 2021). However, an attributable increase in 2011 long rain failure was identified (Lott et al., 2013). Further studies have attributed some African meteorological drought events to large-scale modes of variability, such as the strong 2015 El Niño (Box 11.4; Philip et al., 2018b) and increased SSTs overall (Funk et al., 2015a, 2018b). Natural variability was dominant in the California droughts of 2011–2012 to 2013–2014 (Seager et al., 2015a). In Asia, no climate change signal was found in the record dry spell over Singapore and Malaysia in 2014 (Mcbride et al., 2015) or the drought in central south-west Asia in 2013–2014 (Barlow and Hoell, 2015). Nevertheless, the South East Asia drought of 2015 has been attributed to anthropogenic warming effects (Shiogama et al., 2020). Recent droughts occurring in South America, specifically in the southern Amazon region in 2010 (Shiogama et al., 2013) and in north-east South America in 2014 (Otto et al., 2015b) and 2016 (Martins et al., 2018) were not attributed to anthropogenic climate change. Nevertheless, the central Chile drought between 2010 and 2018 has been suggested to be partly associated to global warming (Boisier et al., 2016; Garreaud et al., 2020). The 2013 New Zealand meteorological drought was attributed to human influence by Harrington et al. (2014, 2016) based on fully coupled CMIP5 models, but no corresponding change in the dry end of simulated precipitation from a stand-alone atmospheric model was found by Angélil et al. (2017).

Event attribution studies also highlight a complex interplay of anthropogenic and non-anthropogenic climatological factors for some events. For example, anthropogenic warming contributed to the 2014 drought in North Eastern Africa by increasing east African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SSTs, causing reduced rainfall (Funk et al., 2015a). As different methodologies, models and data sources have been used for the attribution of precipitation deficits, Angélil et al. (2017) re-examined several events using a single analytical approach and climate model and observational datasets. Their results showed a disagreement in the original anthropogenic attribution in a number of precipitation deficit events, which increased uncertainty in the attribution of meteorological droughts events.

There is a growing number of studies on the detection and attribution of long-term changes in soil moisture deficits. Mueller and Zhang (2016) concluded that anthropogenic forcing contributed significantly to soil moisture drying in the warm season in the Northern Hemisphere from 1951 to 2005 and also led to an increase in the land surface area affected by soil moisture deficits, which can be reproduced by CMIP5 models only if anthropogenic forcings are involved. Gu et al. (2019b) similarly identified a global-scale soil moisture drying tendency in land surface model data from the Global Land Data Assimilation System 2 over the time frame 1948–2005, which was attributed to anthropogenic forcing based on evaluation with CMIP5 models using optimal fingerprinting. Padrón et al. (2019) analysed long-term reconstructed and CMIP5 simulated dry season water availability, defined as precipitation minus ET (i.e., equivalent to soil moisture and runoff availability), also related to agricultural and ecological droughts. They found an intensification of dry-season precipitation minus evapotranspiration deficits over a predominant fraction of the land area in the last three decades, which can only be explained by anthropogenic forcing and is mostly related to increases in ET. Similarly, Williams et al. (2020) concluded that human-induced climate change contributed to the strong soil moisture deficits recorded in the last two decades in Western North America through VPD increases associated with higher air temperatures and lower air humidity. There are few studies analysing the attribution of particular episodes of soil moisture deficits to anthropogenic influence. Nevertheless, the available modelling studies coincide in supporting an anthropogenic attribution associated with more extreme temperatures, exacerbating AED and increasing ET, and thus depleting soil moisture, as observed in southern Europe in 2017 (García-Herrera et al., 2019) and in Australia in 2018 (Lewis et al., 2020) and 2019 (van Oldenborgh et al., 2021), the latter event having strong implications in the propagation of widespread megafires (Nolan et al., 2020).

It is often difficult to separate the role of climate trends from changes in land use, water management and demand for changes in hydrological deficits, especially on a regional scale. However, a global study based on a recent multi-model experiment with global hydrological models and covering several AR6 regions suggests a dominant role of anthropogenic radiative forcing for trends in low, mean and high flows, while simulated effects of water and land management do not suffice to reproduce the observed spatial pattern of trends (Gudmundsson et al., 2021). Regional studies also suggest that climate trends have been dominant compared to land use and human water management for explaining trends in hydrological droughts in some regions, for instance in Ethiopia (Fenta et al., 2017), China (Xie et al., 2015), and North America for the Missouri and Colorado basins, as well as in California (Shukla et al., 2015; Udall and Overpeck, 2017; Ficklin et al., 2018; K. Xiao et al., 2018; Glas et al., 2019; Martin et al., 2020; Milly and Dunne, 2020).

In other regions, the influence of human water uses can be more important to explain hydrological drought trends (Y. Liu et al., 2016; Mohammed and Scholz, 2016). There is medium confidence that human-induced climate change has contributed to an increase of hydrological droughts in the Mediterranean (Giuntoli et al., 2013; Vicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also medium confidence that changes in land use and terrestrial water management contributed to these trends (Section 11.9; Teuling et al., 2019; Vicente-Serrano et al., 2019). A global study with a single hydrological model estimated that human water consumption has intensified the magnitude of hydrological droughts by 20–40% over the last 50 years, and that the human water use contribution to hydrological droughts was more important than climatic factors in the Mediterranean, and central USA, as well as in parts of Brazil (Wada et al., 2013). However, Gudmundsson et al. (2021) concluded that the contribution of human water use is smaller than that of anthropogenic climate change to explain spatial differences in the trends of low flows based on a multi-model analysis. There is still limited evidence and thus low confidence in assessing these trends at the scale of single regions, with few exceptions (Section 11.9).

Different studies using atmospheric-based drought indices suggest an attributable anthropogenic signal, characterized by the increased frequency and severity of droughts (Cook et al., 2018), associated to increased AED (Section 11.6.4.2). The majority of studies are based on the PDSI-PM. Williams et al. (2015) and Griffin and Anchukaitis (2014) concluded that increased AED has had an increased contribution to drought severity over the last decades, and played a dominant role in the intensification of the 2012–2014 drought in California. The same temporal pattern and physical mechanism was stressed by Z. Li et al. (2017) in central Asia. Marvel et al. (2019) compared tree ring-based reconstructions of the PDSI-PM over the past millennium with PDSI-PM estimates based on output from CMIP5 models. The comparisons suggested a contribution of greenhouse gas forcing to the changes since the beginning of the 20th century, although characterized with temporal differences that could be driven by temporal variations in the aerosol forcing. This was in agreement with the dominant external forcings of aridification at global scale between 1950 and 2014 (Bonfils et al., 2020). In the Mediterranean region, there is medium confidence of drying attributable to antropogenic forcing as a consequence of the strong AED increase (Gocic and Trajkovic, 2014; Azorin-Molina et al., 2015; Liuzzo et al., 2016; Maček et al., 2018), which has enhanced the severity of drought events (Vicente-Serrano et al., 2014; Stagge et al., 2017; González-Hidalgo et al., 2018). In particular, this effect was identified to be the main driver of the intensification of the 2017 drought that affected south-western Europe, and was attributed to the human forcing (García-Herrera et al., 2019). Nangombe et al. (2020) and L. Zhang et al. (2020) concluded from differences between precipitation and AED that anthropogenic forcing contributed to the 2018 droughts that affected southern Africa and south-eastern China, respectively, principally as consequence of the high AED that characterized these two events.

The SREX (Chapter 3) concluded that there is low confidence in observed long-term (40 years or more) trends in TC intensity, frequency, and duration, and any observed trends in phenomena such as tornadoes and hail; it is likely that extratropical storm tracks have shifted poleward in both the Northern and Southern Hemispheres, and that heavy rainfalls and mean maximum wind speeds associated with TCs will increase with continued greenhouse gas warming; it is likely that the global frequency of TCs will either decrease or remain essentially unchanged, while it is more likely than not that the frequency of the most intense storms will increase substantially in some ocean basins; there is low confidence in projections of small-scale phenomena such as tornadoes and hail storms; and there is medium confidence that there will be a reduced frequency and a poleward shift of mid-latitude cyclones due to future anthropogenic climate change.

Since SREX, several IPCC Reports also assessed storms. The AR5 (Chapter 2, Hartmann et al., 2013) assessment observed with low confidence long-term trends in TC metrics, but revised the statement from SREX to state that it is virtually certain that there are increasing trends in North Atlantic TC activity since the 1970s, with medium confidence that anthropogenic aerosol forcing has contributed to these trends. The AR5 concluded that it is likely that TC precipitation and mean intensity will increase and more likely than not that the frequency of the strongest storms will increase with continued greenhouse gas warming. confidence in projected trends in overall TC frequency remained low. confidence in observed and projected trends in hail storm and tornado events also remained low. The SROCC (Chapter 6, Collins et al., 2019) assessed past and projected TCs and ETCs, supporting the AR5 conclusions with some additional detail. Literature subsequent to AR5 adds support to the likelihood of increasing trends in TC intensity, precipitation, and frequency of the most intense storms, while some newer studies have added uncertainty to projected trends in overall frequency. A growing body of literature since AR5 on the poleward migration of TCs led to a new assessment in SROCC of low confidence that the migration in the western North Pacific represents a detectable climate change contribution from anthropogenic forcing. The SR1.5 (Chapter 3, Hoegh-Guldberg et al., 2018) essentially confirmed the AR5 assessment of TCs and ETCs, adding that heavy precipitation associated with TCs is projected to be higher at 2°C compared to 1.5°C global warming (medium confidence).

The genesis, development, and tracks of TCs depend on conditions of the larger-scale circulations of the atmosphere and ocean (Christensen et al., 2013). Large-scale atmospheric circulations, such as the Hadley and Walker circulations and the monsoon circulations can significantly affect TCs, as can internal variability acting on various time scales (Annex IV), from intra-seasonal (e.g., the Madden–Julian and Boreal Summer Intraseasonal oscillations and equatorial waves) and interannual (e.g., the El Niño–Southern Oscillation and Pacific and Atlantic Meridional Modes), to inter-decadal (e.g., Atlantic Multidecadal Variability and Pacific Decadal Variability). This broad range of natural variability makes detection of anthropogenic effects difficult, and uncertainties in the projected changes of these modes of variability increase uncertainty in the projected changes in TC activity. Aerosol forcing also affects sea surface temperature (SST) patterns and cloud microphysics, and it is likely that observed changes in TC activity are partly caused by changes in aerosol forcing (Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019; Takahashi et al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible changes from these drivers, there is medium confidence that the Hadley cell has widened and will continue to widen in the future (Sections 2.3, 3.3 and 4.5). This likely causes latitudinal shifts of TC tracks (Sharmila and Walsh, 2018). Regional TC activity changes are also strongly affected by projected changes in SST warming patterns (Yoshida et al., 2017), which are highly uncertain (Chapters 4 and 9).

There is general agreement in the literature that anthropogenic greenhouse gases and aerosols have measurably affected observed oceanic and atmospheric variability in TC-prone regions (see Chapter 3). This underpinned the SROCC assessment of medium confidence that humans have contributed to the observed increase in Atlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., 2013). Literature subsequent to AR5 lends further support to this statement (Knutson et al., 2019). However, there is still no consensus on the relative magnitude of human and natural influences on past changes in Atlantic hurricane activity, and particularly on which factor has dominated the observed increase (Ting et al., 2015) and it remains uncertain whether past changes in Atlantic TC activity are outside the range of natural variability. A recent result using high-resolution dynamical model experiments suggested that the observed spatial contrast in TC trends cannot be explained only by multi-decadal natural variability, and that external forcing plays an important role (Murakami et al., 2020).Observational evidence for significant global increases in the proportion of major TC intensities (Kossin et al., 2020) is consistent with both theory and numerical modelling simulations, which generally indicate an increase in mean TC peak intensity and the proportion of very intense TCs in a warming world (Knutson et al., 2015, 2020; Walsh et al., 2015, 2016). In addition, high-resolution coupled model simulations provide support that natural variability alone is unlikely to explain the magnitude of the observed increase in TC intensification rates and upward TC intensity trend in the Atlantic basin since the early 1980s (Bhatia et al., 2019; Murakami et al., 2020).

The cause of the observed slowdown in TC translation speed is not yet clear. Yamaguchi et al. (2020) used large ensemble simulations to argue that part of the slowdown is due to actual latitudinal shifts of TC tracks, rather than data artefacts, in addition to atmospheric circulation changes. G. Zhang et al. (2020) used large ensemble simulations to show that anthropogenic forcing can lead to a robust slowdown, particularly outside of the tropics at higher latitudes. Yamaguchi and Maeda (2020b) found a significant slowdown in the western North Pacific over the past 40 years and attributed the slowdown to a combination of natural variability and global warming. The slowing trend since 1900 over the USA is robust and significant after removing multi-decadal variability from the time series (Kossin, 2019). Among the hypotheses discussed is the physical linkage between warming and slowing circulation (Held and Soden, 2006; see also Section 8.2.2.2), with expectations of Arctic amplification and weakening circulation patterns through weakening meridional temperature gradients (Coumou et al., 2018; see also Cross-Chapter Box 10.1), or through changes in planetary wave dynamics (Mann et al., 2017). The tropics expansion and the poleward shift of the mid-latitude westerlies associated with warming is also suggested as the reason of the slowdown (G. Zhang et al., 2020). However, the connection of these mechanisms to the slowdown has not been robustly shown. Furthermore, slowing trends have not been unambiguously observed in circulation patterns that steer TCs, such as the Walker and Hadley circulations (Section 2.3.1.4), although these circulations generally slow down in numerical simulations under global warming (Sections 4.5.1.6 and 8.4.2.2).

The observed poleward trend in western North Pacific TCs remains significant after accounting for the known modes of dominant interannual to decadal variability in the region (Kossin et al., 2016a), and is also found in CMIP5 model-simulated TCs (in the recent historical period 1980–2005), although it is weaker than observed and is not statistically significant (Kossin et al., 2016a). However, the trend is significant in 21st-century CMIP5 projections under the RCP8.5 scenario, with a similar spatial pattern and magnitude to the past observed changes in that basin over the period 1945–2016, supporting a possible anthropogenic greenhouse gas contribution to the observed trends (Kossin et al., 2016a; Knutson et al., 2019).

The recent active TC seasons in some basins have been studied to determine whether there is anthropogenic influence. For 2015, Murakami et al. (2017b) explored the unusually high TC frequency near Hawaii and in the eastern Pacific basin. W. Zhang et al. (2016b) considered unusually high Accumulated Cyclone Energy (ACE) in the western North Pacific; and S.-H. Yang et al. (2018) and Yamada et al. (2019) looked at TC intensification in the western North Pacific. These studies suggest that the anomalous TC activity in 2015 was not solely explained by the effect of an extreme El Niño (see Box 11.4) and that there was also an anthropogenic contribution, mainly through the effects of SSTs in subtropical regions. In the post-monsoon seasons of 2014 and 2015, tropical storms with lifetime maximum winds greater than 46 m s⁻¹ were first observed over the Arabian Sea, and Murakami et al. (2017a) showed that the probability of late-season severe tropical storms is increased by anthropogenic forcing compared to the preindustrial era. Murakami et al. (2018) concluded that the active 2017 Atlantic hurricane season was mainly caused by pronounced SSTs in the tropical North Atlantic and that these types of seasonal events will intensify with projected anthropogenic forcing. The trans-basin SST change, which might be driven by anthropogenic aerosol forcing, also affects TC activity. Takahashi et al. (2017) suggested that a decrease in sulphate aerosol emissions caused about half of the observed decreasing trends in TC genesis frequency in the south-eastern region of the western North Pacific during 1992–2011.

Event attribution is used in TC case studies to test whether the severities of recent intense TCs are explained without anthropogenic effects. In a case study of Hurricane Sandy (2012), Lackmann (2015) found no statistically significant impact of anthropogenic climate change on storm intensity, while projections in a warmer world showed significant strengthening. However, Magnusson et al. (2014) found that, in European Centre for Medium-Range Weather Forecast (ECMWF) simulations, the simulated cyclone depth and intensity, as well as precipitation, were larger when the model was driven by the warmer actual SSTs than the climatological average SSTs. In Super Typhoon Haiyan, which struck the Philippines on 8 November 2013, Takayabu et al. (2015) took an event attribution approach with cloud system-resolving (around 1 km) downscaling ensemble experiments to evaluate the anthropogenic effect on typhoons, and showed that the intensity of the simulated worst-case storm in the actual conditions was stronger than that in a hypothetical condition without historical anthropogenic forcing in the model. However, in a similar approach with two coarser parametrized convection models, Wehner et al. (2019) found conflicting human influences on Haiyan’s intensity. Patricola and Wehner (2018) found little evidence of an attributable change in intensity of hurricanes Katrina (2005), Irma (2017), and Maria (2017) using a regional climate model configured between 3 km and 4.5 km resolution. They did, however, find attributable increases in heavy precipitation totals. These results imply that higher resolution, such as in a convective permitting 5 km or less mesh model, is required to obtain a robust anthropogenic intensification of a strong TC by simulating realistic rapid intensification (Kanada and Wada, 2016; Kanada et al., 2017a), and that whether the TC intensification can be attributed to the recent warming depends on the case.

The dominant factor in the extreme rainfall amounts during Hurricane Harvey’s passage onto the USA in 2017 was its slow translation speed. But studies published after the event have argued that anthropogenic climate change contributed to an increase in rain rate, which compounded the extreme local rainfall caused by the slow translation. Emanuel (2017) used a large set of synthetically-generated storms and concluded that the occurrence of extreme rainfall as observed in Harvey was substantially enhanced by anthropogenic changes to the larger-scale ocean and atmosphere characteristics; Trenberth et al. (2018) linked Harvey’s rainfall totals to the anomalously large ocean heat content from the Gulf of Mexico; and van Oldenborgh et al. (2017) and Risser and Wehner (2017) applied extreme value analysis to extreme rainfall records in the Houston, Texas region, both attributing large increases to climate change. Large precipitation increases during Harvey due to global warming were also found using climate models (van Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018). Harvey precipitation totals were estimated in these papers to be three to 10 times more probable due to climate change. A best estimate from a regional climate and flood model is that urbanization increased the risk of the Harvey flooding by a factor of 21 (W. Zhang et al., 2018), using a regional climate and flood model, found that surface roughness from urbanization increased the risk of the Harvey flooding by a factor of 21. Anthropogenic effects on precipitation increases were also predicted in advance from a forecast model for Hurricane Florence in 2018 (Reed et al., 2020).

In summary, it is very likely that the recent active TC seasons in the North Atlantic, the North Pacific, and Arabian basins cannot be explained without an anthropogenic influence. The anthropogenic influence on these changes is principally associated to aerosol forcing, with stronger contributions to the response in the North Atlantic. It is more likely than not that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing. It is likely that the poleward migration of TCs in the western North Pacific and the global increase in TC intensity rates cannot be explained entirely by natural variability. Event attribution studies of specific strong TCs provide limited evidence for anthropogenic effects on TC intensifications so far, but high confidence for increases in TC heavy precipitation. There is high confidence that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (2017) and other intense TCs.

The spatial extent, or ‘size’, of the TC wind field is an important determinant of storm surge and damage. No detectable anthropogenic influences on TC size have been identified to date, because TCs in observations vary in size substantially (Chan and Chan, 2015) and there is no definite theory on what controls TC size, although this is an area of active research (Chavas and Emanuel, 2014; Chan and Chan, 2018). However, projections by high-resolution models indicate future broadening of TC wind fields when compared to TCs of the same categories (Yamada et al., 2017), while Knutson et al. (2015) simulate a reasonable interbasin distribution of TC size climatology, but project no statistically significant change in global average TC size. A plausible mechanism is that, as the tropopause height becomes higher with global warming, the eye wall areas become wider because the eye walls are inclined outward with height to the tropopause. This effect is only reproduced in high-resolution convection-permitting models capturing eye walls, and such modelling studies are not common. Moreover, the projected TC size changes are generally on the order of 10% or less, and these size changes are still highly variable between basins and studies. Thus, the projected change in both magnitude and sign of TC size is uncertain.

The coastal effects of TCs depend on TC intensity, size, track, and translation speed. Projected increases in sea level, average TC intensity, and TC rainfall rates each generally act to further elevate future storm surge and fresh-water flooding (see Section 9.6.4.2). Changes in TC frequency could contribute toward increasing or decreasing future storm surge risk, depending on the net effects of changes in weaker vs stronger storms. Several studies (McInnes et al., 2014, 2016; Little et al., 2015; Garner et al., 2017; Timmermans et al., 2017, 2018) have explored future projections of storm surge in the context of anthropogenic climate change with the influence of both sea level rise and future TC changes. Garner et al. (2017) investigated the near-future changes in the New York City coastal flood hazard, and suggested a small change in storm-surge height because effects of TC intensification are compensated by the offshore shifts in TC tracks, but concluded that the overall effect due to the rising sea levels would increase the flood hazard. Future projection studies of storm surge in East Asia, including China, Japan and Korea, also indicate that storm surges due to TCs become more severe (J.A. Yang et al., 2018; Mori et al., 2019, 2021; J. Chen et al., 2020b). For the Pacific Islands, McInnes et al. (2014) found that the future projected increase in storm surge in Fiji is dominated by sea level rise, and projected TC changes make only a minor contribution. Among various storm surge factors, there is high confidence that sea level rise will lead to a higher possibility of extreme coastal water levels in most regions, with all other factors assumed equal.

The probability of co-occurring meteorological droughts and heatwaves has increased in the observational period in many regions and will continue to do so under unabated warming (Herrera-Estrada and Sheffield, 2017; Zscheischler and Seneviratne, 2017; Hao et al., 2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et al., 2021). Overall, projections of increases in co-occurring drought and heatwaves are reported in northern Eurasia (Schubert et al., 2014), Europe (Orth et al., 2016a; Sedlmeier et al., 2018), south-east Australia (Kirono et al., 2017), multiple regions of the USA (Diffenbaugh et al., 2015; Herrera-Estrada and Sheffield, 2017), north-west China (X. Li et al., 2019; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The dominant signal is related to the increase in heatwave occurrence, which has been attributed to anthropogenic forcing (Section 11.3.4). This means that, even if drought occurrence is unaffected, compound hot and dry events will be more frequent (Sarhadi et al., 2018; Yu and Zhai, 2020).

Drought and heatwaves are also associated with fire weather, related through high temperatures, low soil moisture, and low humidity. Fire weather refers to weather conditions conducive to triggering and sustaining wildfires, which generally include temperature, soil moisture, humidity, and wind (Chapter 12). Concurrent hot and dry conditions amplify conditions that promote wildfires (Schubert et al., 2014; Littell et al., 2016; Dowdy, 2018; Hope et al., 2019). Burnt area extent in western USA forests (Abatzoglou and Williams, 2016) and particularly in California (Williams et al., 2019) has been linked to anthropogenic climate change via a significant increase in vapour pressure deficit, a primary driver of wildfires. A study of the western USA examined the correlation between historical water-balance deficits and annual area burned, across a range of vegetation types, from temperate rainforest to desert (McKenzie and Littell, 2017). The relationship between temperature and dryness, and wildfire, varied with ecosystem type, and the fire–climate relationship was nonstationary and vegetation-dependent. In many fire-prone regions, such as the Mediterranean and China’s Daxing’anling region, projections for increased severity of future drought and heatwaves may lead to an increased frequency of wildfires relative to observed climatology (Tian et al., 2017; Ruffault et al., 2018). Observations show a long-term trend towards more dangerous weather conditions for bushfires in many regions of Australia, which is attributable (at least in part) to anthropogenic climate change (Dowdy, 2018). There is emerging evidence that recent regional surges in wildland fires are being driven by changing weather extremes (Cross-Chapter Box 3; Jia et al., 2019; SRCCL Chapter 2). Between 1979 and 2013, the global burnable area affected by long fire weather seasons doubled, and the mean length of the fire weather season increased by 19% (Jolly et al., 2015). However, at the global scale, the total burned area has been decreasing between 1998 and 2015 due to human activities mostly related to changes in land use (Andela et al., 2017). Given the projected high confidence increase in compound hot and dry conditions, there is high confidence that fire weather conditions will become more frequent at higher levels of global warming in some regions. This assessment is also consistent with Chapter 12’s examination of regional projected changes in fire weather. The SRCCL (Chapter 2) assessed with high confidence that future climate variability is expected to enhance the risk and severity of wildfires in many biomes such as tropical rainforests.

El Niño–Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different parts of the world, especially in extreme El Niño (Annex IV.2.3) cases. Additionally, the background climate warming associated with greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Niño conditions. The 2015–2016 extreme El Niño event was one of the three extreme El Niño events since the 1980s and the availability of satellite rainfall observations. According to some measures, it was the strongest El Niño in the past 145 years (Barnard et al., 2017). The 2015–2016 warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S, 150°E–150°W), and this exceptional warmth was unlikely to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend (Newman et al., 2018). In particular, its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing to the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium confidence) (Section 2.4.2), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. A brief summary of extreme events that happened in 2015–2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of the world during the 2015–2016 extreme El Niño in Box 11.4, Table 1, as well as in the short summary that follows.

Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, and Europe. As a result, global measurements of land water anomalies were particularly low in that year (Humphrey et al., 2018). In 2015, Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze crisis(Fieldet al. , 2016; Huijnenet al. , 2016; Patraet al. , 2017; Hartmannet al. , 2018). The northern part of Australia experienced high temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the Gulf of Carpentaria in Northern Australia (Duke et al., 2017). The Amazon region experienced the most intense droughts of this century in 2015–2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis et al., 2011; Erfanian et al., 2017; Panisset et al., 2018). The 2015–2016 Amazon drought impacted the entirety of South America north of 20°S during the austral spring and summer (Erfanian et al., 2017). It also increased forest fire incidence by 36% compared to the preceding 12 years (Aragão et al., 2018) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) concentration in the area, affecting air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation of food, medicines, and fuels (INMET, 2017). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the worst in several decades and was associated with the 2015–2016 extreme El Niño (Blunden and Arndt, 2016; Philip et al., 2018b). It was suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local air temperatures (Funk et al., 2016, 2018b; Yuan et al., 2018a). It has also been suggested that the 2015–2016 extreme El Niño affected circulation patterns in Europe during the 2015–2016 winter (Geng et al., 2017; Scaife et al., 2017).

In 2015, tropical cyclone activity was notably high in the North Pacific (Blunden and Arndt, 2016). Over the western North Pacific, there were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area’s typical annual value of 6.3 (W. Zhang et al., 2016b). Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain (Collins et al., 2016; Murakami et al., 2017b). These extraordinary TC activities were related to the average SST anomaly during that year, which were associated with the 2015–2016 extreme El Niño and the positive phase of the Pacific Meridional Mode (Murakami et al., 2017b; Hong et al., 2018; Yamada et al., 2019). However, it has been suggested that the intense TC activities in both the western and the eastern North Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing (Murakami et al., 2017b; S.-H. Yang et al., 2018). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity in the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in Australia, it was the least active TC season since satellite records began in 1969–1970 (Blunden and Arndt, 2017).

This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical High in this month (Shimpo et al., 2019; Ren et al., 2020), which caused extreme conditions from Europe, Eurasia, and North America (Box 11.4, Figure 2; Kornhuber et al., 2019). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) and the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al. , 2019). A role of Atlantic SST anomaly on the meandering jets and the subtropical high have been suggested (B. Liu et al., 2019). These dynamic and thermodynamic components generally have substantial influence on extreme rainfall in East Asia (Oh et al., 2018), but it is under investigation whether these factors were due to anthropogenic forcing.

Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May–July period, Vogel et al. (2019) found that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated and agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1°C climate which was the estimated global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May–July temperature extremes would approximately occur two out of three years under +1.5°C of global warming, and every year under +2°C of global warming. Imada et al. (2019) also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels. Kawase et al. (2020) showed that the extreme rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic warming. Hence, it is virtually certain that these 2018 concurrent events would not have occurred without human-induced global warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, there is currentlylow confidence in projected changes in the frequency or strength of the anomalous circulation patterns leading to concurrent extremes (e.g., Cross-Chapter Box 10.1).

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